Sewade Ogun
email: sewade.ogun@inria.fr

Vincent Colotte
email: vincent.colotte@loria.fr

Emmanuel Vincent
email: emmanuel.vincent@inria.fr

Stochastic Pitch Prediction Improves the Diversity and Naturalness of Speech in Glow-TTS

Keywords: to-speech, pitch prediction, generative models

Flow-based generative models are widely used in text-tospeech (TTS) systems to learn the distribution of audio features (e.g., Mel-spectrograms) given the input tokens and to sample from this distribution to generate diverse utterances. However, in the zero-shot multi-speaker TTS scenario, the generated utterances lack diversity and naturalness. In this paper, we propose to improve the diversity of utterances by explicitly learning the distribution of fundamental frequency sequences (pitch contours) of each speaker during training using a stochastic flowbased pitch predictor, then conditioning the model on generated pitch contours during inference. The experimental results demonstrate that the proposed method yields a significant improvement in the naturalness and diversity of speech generated by a Glow-TTS model that uses explicit stochastic pitch prediction, over a Glow-TTS baseline and an improved Glow-TTS model that uses a stochastic duration predictor.

Introduction

Multi-speaker text-to-speech (TTS) systems have significantly improved in quality and naturalness over the years. Given the one-to-many mapping between phonemes and a speaker's utterance, and other variabilities which improve the perceived quality of speech, some systems model these variabilities by explicitly learning the speaker's characteristics such as pitch, energy, or rhythm [START_REF] Ren | FastSpeech 2: Fast and high-quality end-to-end text to speech[END_REF][START_REF] Łańcucki | Fastpitch: Parallel text-to-speech with pitch prediction[END_REF]. Instead, flow-based TTS systems learn the distribution of the Mel-spectrogram (or audio), which includes all the speaker's characteristics, given the input tokens and a speaker embedding [START_REF] Kim | Glow-TTS: A generative flow for text-to-speech via monotonic alignment search[END_REF][START_REF] Valle | Flowtron: An autoregressive flow-based generative network for text-to-speech synthesis[END_REF][START_REF] Kim | Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech[END_REF][START_REF] Miao | Flow-TTS: A non-autoregressive network for text to speech based on flow[END_REF]. Flow-based generative models are able to fit complex data distributions and generate diverse utterances with high quality. In addition, they eliminate the problem of over-smoothness in generated Mel-spectrograms [START_REF] Ren | Revisiting oversmoothness in text to speech[END_REF], and therefore, they do not require a post-processing stage for generated Mel-spectrograms, as is found in many non-flow-based non-generative models, e.g., [START_REF] Shen | Natural TTS synthesis by conditioning wavenet on mel spectrogram predictions[END_REF].

However, in the zero-shot multi-speaker TTS scenario, flow-based models are unable to learn the diversity of speaking styles inherent to each speaker given only the speaker embeddings. In particular, when these models are used to generate speech for unseen speakers, the generated utterances often sound monotonic and less natural. Our goal is to design an improved flow-based architecture that is able to match the diversity of real speech, including for unseen speakers.

Many methods have been proposed in the literature for controlling the expressivity of speakers. These methods use global style tokens or embeddings learned during training [START_REF] Wang | Style Tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis[END_REF][START_REF] Valle | Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens[END_REF][START_REF] Min | Meta-stylespeech: Multi-speaker adaptive text-to-speech generation[END_REF] to control the speaking style. Style tags [START_REF] Kim | Expressive text-to-speech using style tag[END_REF] have also been used to transfer speaking styles from text embeddings to speech utterances, while other studies, e.g., [START_REF] Kulkarni | Improving transfer of expressivity for end-to-end multispeaker text-to-speech synthesis[END_REF], have attempted to transfer emotions from one speaker to another using an expressivity encoder. These methods generally aim to mimic the style of a reference utterance or text, which is different from our goal.

Several TTS models also explicitly model speech properties such as duration, energy, timbre, rhythm, or speaking rate. In FastSpeech2 [START_REF] Ren | FastSpeech 2: Fast and high-quality end-to-end text to speech[END_REF] and FastPitch [START_REF] Łańcucki | Fastpitch: Parallel text-to-speech with pitch prediction[END_REF], the duration, pitch contour, and energy of the input tokens are explicitly learned. ZSM-SS [START_REF] Kumar | Normalization driven zero-shot multi-speaker speech synthesis[END_REF] uses a similar approach with external speaker embeddings, but it normalizes the prosodic features (e.g., energy and pitch) in the affine layers of the model to improve speaker similarity in the zero-shot multi-speaker setting. Since these systems are deterministic by design, they are unable to output diverse renderings for a given text input and speaker. Recently, Varianceflow [START_REF] Lee | Varianceflow: High-quality and controllable text-to-speech using variance information via normalizing flow[END_REF] replaced the deterministic pitch and energy predictors in a FastSpeech2 TTS model with normalizing flows. The work showed the effectiveness of using normalizing flows to learn the prosodic features, however, it differs from ours as it was proposed for a single-speaker, non-generative TTS model. [START_REF] Casanova | SC-GlowTTS: An efficient zero-shot multi-speaker textto-speech model[END_REF] is a zero-shot multi-speaker TTS system that conditions a Glow-TTS model on external speaker embeddings. SNAC [START_REF] Choi | SNAC: Speakernormalized affine coupling layer in flow-based architecture for zero-shot multi-speaker text-to-speech[END_REF], also a flow-based zero-shot multi-speaker TTS system, uses a coupling layer that explicitly normalizes the input by the parameters predicted from a speaker embedding vector. VITS [START_REF] Kim | Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech[END_REF], an end-to-end flow-based TTS model, introduced a stochastic flow-based duration predictor to generate diverse rhythms from the input text, which was found to improve the rhythm of generated utterances over a deterministic duration predictor. Your-TTS extends VITS to the multi-speaker multilanguage scenario, enabling the generation of utterances for unseen speakers in several languages. Although all these models inherently enable sampling from the latent representation of the input tokens to generate diverse utterances, the quality of generated utterances in the multi-speaker setup is still worse than that of single-speaker TTS models [START_REF] Ren | Revisiting oversmoothness in text to speech[END_REF].

SC-GlowTTS

The focus of this work is on generating realistic and diverse utterances for speakers unseen during training by explicitly learning the pitch distribution. To do so, we enhance a Glow-TTS system by integrating a stochastic duration predictor and a stochastic pitch predictor into the model. The stochastic duration predictor learns more realistic phoneme duration and gives better speech rhythm to the utterances while the pitch predictor learns the distribution of pitch contours in the dataset. These provide a method to generate diverse and natural utterances for different unseen speakers at inference time.

Section 2 presents the proposed improved Glow-TTS architecture. Sections 3 and 4 describe the experimental setup and the results. We conclude in Section 5.

Improved Glow-TTS Architecture

The classical Glow-TTS architecture comprises a Transformerbased encoder, a flow-based decoder, and a deterministic duration predictor. The Transformer encoder generates contextual phonetic embeddings from the input tokens, which are then linearly projected into an 80-dimensional representation of the mean µ of the prior distribution. Given µ, a scalar noise temperature T , and a random vector ϵ sampled from the standard normal distribution, the latent representation z sampled from the prior distribution can be expressed as

z = µ + T ϵ. (1)
At training time, only the mean µ is predicted and the temperature T is fixed to 1. At inference time, a certain value of T (often smaller than 1) is chosen, and a latent representation is sampled from the prior distribution as input to the decoder to generate a Mel-spectrogram. Figure 1 illustrates the proposed improved architecture with arrows showing the computation during inference. This architecture incorporates a stochastic duration predictor and a novel stochastic pitch predictor, and it requires changes to the decoder.

Decoder

The decoder is a stack of normalizing flow blocks, each consisting of an activation normalization layer, an invertible 1x1 convolution layer, and an affine coupling layer. Classically, in the multi-speaker TTS setup, the decoder is conditioned on speaker embeddings through the affine coupling layers. At training time, the decoder inverts the Mel-spectrogram into a hidden representation, which is aligned to the latent representation from the encoder using an alignment search algorithm called Monotonic Alignment Search (MAS). MAS finds the most probable monotonic alignment between the hidden representation and the mean of the prior distribution. In this work, the decoder is additionally conditioned on the pitch information. We use the log-F0 values extracted from the ground-truth Mel-spectrograms as pitch targets, and set all unvoiced values to zero during training. The log-F0 is first projected to match the dimension of the decoder features using a 1D convolution layer, which is then used to condition the decoder features through the affine coupling layers of the decoder. We squeeze the projected log-F0 features to match the squeeze ratio of the original decoder features. At inference, the predicted log-F0 values generated by the stochastic pitch predictor are used for conditioning.

Duration Prediction

The classical Glow-TTS model predicts the speaker-dependent duration of each token through conditioning on speaker embeddings. The predicted durations have been found to yield unnatural rhythm and cannot express how a person speaks at different speaking rates [START_REF] Kim | Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech[END_REF].

To generate a more human-like rhythm, we replace the deterministic predictor by the stochastic flow-based duration predictor1 in VITS [START_REF] Kim | Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech[END_REF]. This model is composed of two normalizing flow stages that learn the discrete duration estimated using the MAS alignment algorithm. At training time, a first-stage set of flows (called post flows) inverts the discrete duration which is conditioned on the speaker and input embeddings into a dediscretized distribution of log-duration, then the second-stage set of flows inverts these log-durations into a Gaussian noise. 2At inference time, the post-flows are discarded. A Gaussian noise vector is simply sampled and passed through the secondstage flows to generate log-durations.

Pitch Prediction

Utterances with diverse stress and intonation can be generated by sampling different ϵ in (1). In the multi-speaker TTS scenario, the resulting distribution of stress and intonation varies depending on the speaker embedding. We found that this alone is not enough to produce diverse, natural, and lively utterances for speakers unseen in training.

Thus, we use an explicit flow-based stochastic pitch predictor to learn pitch contours that closely match the distribution of real contours. This model is made up of 4 blocks of 1D convolution, dilated and depth-separable convolutions and spline flows [START_REF] Durkan | Neural spline flows[END_REF]. Variational data augmentation [START_REF] Chen | VFlow: More expressive generative flows with variational data augmentation[END_REF] is applied to increase the dimension of the scalar pitch inputs, as this is necessary to perform high-dimensional transformation. Specifically, an extra vector of Gaussian noise is concatenated to the pitch vector or input Gaussian noise as padding, before passing them through the block of flows. Our architecture is borrowed from the second set of flow blocks of the stochastic duration predictor.

During training, the speaker-conditioned input embeddings are expanded to match the duration of each input token estimated by MAS. These are then used as input to the flow-based stochastic pitch predictor to reverse the log-F0 values into Gaussian noise. The model is optimized jointly with other modules via maximum likelihood estimation. 2 At inference, the speakerconditioned input embeddings are expanded using the predicted duration values, while a Gaussian noise is sampled to generate log-F0 values for the decoder, as shown in Fig. 1.

Experimental Setup

We conducted objective and subjective evaluations on three variants of the model: the baseline Glow-TTS (54.5 M parameters), Glow-TTS with stochastic duration predictor (GlowTTS-STD, 55.3 M parameters), and Glow-TTS with stochastic duration and pitch predictors (GlowTTS-STDP, 56.0 M parameters).

Dataset Preparation

The Common Voice dataset (English subset, version 7.0) [START_REF] Ardila | Common Voice: A massively-multilingual speech corpus[END_REF] was used to train all the models. Common Voice is a crowdsourced, read speech dataset with very large speaker diversity. The stochastic pitch predictor can benefit from learning a diverse distribution of F0 values for speakers in different contexts from the dataset. The dataset was filtered by selecting only high-quality speakers based on speaker-level Mean Opinion Scores (MOS) estimated by a MOS estimator [START_REF] Ogun | Can we use common voice to train a multi-speaker TTS system?[END_REF]. We used the resulting filtered 4,469-speaker, 230.75 h dataset for model training and validation. 512 samples were randomly selected from the dataset for validation. The dataset was resampled from 48 kHz or 32 kHz to 16 kHz, and silences and long pauses were removed using a voice activity detection tool. 3F0 contours were extracted from the training and validation sets using pYIN [START_REF] Mauch | pYIN: A fundamental frequency estimator using probabilistic threshold distributions[END_REF] with 64 ms window size and 16 ms hop size to match the parameters of the Mel-spectrogram. We found it suitable in order to aid the correct alignment of the extracted F0 values to the input embeddings during model training. In addition, a speaker embedding was pre-computed for each utterance from a speaker verification model4 trained on Voxceleb [START_REF] Nagrani | Voxceleb: Large-scale speaker verification in the wild[END_REF]. The embeddings are l2-normalized, 256-dimensional vectors. Phonemes (without stress) and characters were used as input tokens in a mixed approach [START_REF] Kastner | Representation mixing for TTS synthesis[END_REF]. The input tokens were interspersed with a blank token.

Training and Inference Hyperparameters

All TTS models were trained in automatic mixed-precision mode using a global batch size of 192 on 4 Nvidia RTX GPUs. Optimization was done using the RAdam optimizer [START_REF] Liu | On the variance of the adaptive learning rate and beyond[END_REF] with a learning rate of 0.001, and a cosine-annealing scheduler with a linear warm-up of 6,000 steps. It took approximately 4 days for the models with stochastic pitch prediction and stochastic duration prediction to completely train while the baseline Glow-TTS was trained for 2 days. Each model was trained for 200 epochs, and the best model was selected at the end of the training run using the validation loss.

At inference time, the noise temperature of the prior distribution was set to 0.667 for all models as in Glow-TTS [START_REF] Kim | Glow-TTS: A generative flow for text-to-speech via monotonic alignment search[END_REF]. The noise temperature for the pitch predictor was set to 0.8, similar to VITS [START_REF] Kim | Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech[END_REF]. The noise temperature for the duration predictor was also set to 0.8 for speaking style evaluation and to 1.0 for reading style evaluation (see Section 3.3).

A 16 kHz HiFi-GAN V1 vocoder [START_REF] Kong | Hifi-GAN: Generative adversarial networks for efficient and high fidelity speech synthesis[END_REF] trained on the Lib-riTTS dataset [START_REF] Zen | LibriTTS: A corpus derived from LibriSpeech for text-to-speech[END_REF] was used to convert the generated Melspectrograms into audio. The vocoder was not finetuned on Mel-spectrograms generated by the models. All experiments were performed using the NeMo toolkit [START_REF] Kuchaiev | NeMo: A toolkit for building AI applications using neural modules[END_REF].

Subjective Evaluation

For all the subjective evaluations, 3 unseen male (p245, p254, and p263) and 3 unseen female speakers (p228, p231, and p250) were selected from the VCTK dataset [START_REF] Veaux | CSTR VCTK corpus: English multi-speaker corpus for CSTR voice cloning toolkit[END_REF]. 5 Firstly, the naturalness of the utterances generated by each model both in speaking style (N-MOS) and reading style (NR-MOS) were evaluated subjectively. We selected 3 short (3-4 s) text sentences from VCTK for the N-MOS evaluation, and 3 long (9-13 s) text sentences with punctuation from LibriTTS for the NR-MOS evaluation to generate 18 short and 18 long utterances per model. The evaluators were asked to rate how likely these are real speaking and read speech, respectively. Secondly, the speaker similarity (S-MOS) of the generated utterances to a reference utterance was evaluated. For this, 3 utterances that were recorded by each of the selected speakers in the VCTK dataset were used. The VCTK reference utterances were re-synthesized using the pretrained vocoder (VCTK-copy) and were added to the subjective evaluations to determine the upper bound on the scores. Lastly, the diversity of generated utterances (D-MOS) was evaluated for each model. For each speaker, 3 utterances were generated from the same text prompt and concatenated. The evaluators were asked to rate the diversity of the utterances in terms of the intonation (flow of pitch) of the speaker. Mean opinion score evaluations were performed on a 1-5 Likert scale. In total, 26 volunteers participated in the evaluation.

Objective Evaluation

Objective evaluations were carried out similarly to subjective evaluations. The overall quality (WV-MOS) of the utterances generated by each model was evaluated using a MOS predictor [30, App. C]. 6 Then, the cosine similarity (cos-sim) between the generated utterances and the reference speaker embeddings was computed. Furthermore, we compare the distribution of log-F0 values of the generated utterances to the reference VCTK audio samples. For this evaluation, 10 utterances from each of 10 unseen male speakers and 10 unseen female speakers were selected from the VCTK dataset. 5

Results

Table 1 shows the results of subjective and objective evaluations. The standard deviations of N-MOS, NR-MOS, and S-MOS scores lie between 1.00 and 1.24, while those of WV-MOS and cos-sim scores are approximately 0.3 and 0.04 respectively for each model. The best models have been selected (and highlighted) in each row by computing the 95% confidence intervals (CI) on the difference between a pair of models. The difference is statistically significant if the confidence interval lies fully on the positive side of the real axis.

Naturalness and Quality of Utterances

We found a significant improvement in the naturalness (N-MOS) of speaking utterances generated by GlowTTS-STD and GlowTTS-STDP over the baseline model. This shows that the stochastic duration predictor more accurately learns the speaking style of speakers than the baseline duration predictor. In addition, stochastic pitch prediction does not degrade the naturalness of utterances, and in particular, improves or maintains the naturalness of utterances. For unseen male speakers, we see an improvement of 0.29 MOS points over GlowTTS-STD.

For longer sentences, the utterances generated by Glow-TTS-STD and GlowTTS-STDP are rated higher than the base-Table 1: N-MOS, NR-MOS, S-MOS of utterances generated for 6 unseen speakers by the baseline Glow-TTS, GlowTTS-STD, GlowTTS-STDP, and VCTK-copy. Bold numbers denote the best system in each row and the systems statistically equivalent to it. Here, utterances generated by GlowTTS-STDP for female speakers are rated higher than utterances generated by GlowTTS-STD, but there is no significant difference in the ratings for male speakers. In general, short utterances were rated higher than longer utterances. Nevertheless, GlowTTS-STDP still shows significant improvement in reading-style naturalness over the baseline. In addition, the utterances generated by GlowTTS-STD and GlowTTS-STDP have higher overall quality (WV-MOS), however, the ratings are still a distance away from the VCTK utterances with copy synthesis, which were rated higher in general.

Diversity of Utterances

In Figure 2, we see an increasing trend in the diversity of utterances (D-MOS) from the baseline to GlowTTS-STDP, with GlowTTS-STDP being able to generate more diverse utterances than the other models without loss of naturalness. In general, female utterances were rated higher in diversity than male ones. Figure 3 shows the distribution of log-F0 values computed for the generated utterances. Here, we see that the distribution of log-F0 values in GlowTTS-STDP utterances better matches those of real speech for both male and female speakers with more spread-out log-F0 values, while those of the baseline and GlowTTS-STD are narrower, indicating less variability in their predicted log-F0 values.

Speaker Similarity

The utterances generated by models using a stochastic duration predictor (GlowTTS-STD and GlowTTS-STDP) exhibit signif- icantly higher speaker similarity (S-MOS) than the baseline, especially for female speakers. We believe that the rhythm of speech, controlled by the stochastic duration predictor, significantly impacts the subjective speaker similarity ratings given by volunteers. Similarly, the cos-sim values are higher for GlowTTS-STD and GlowTTS-STDP, however this is not as significant as the S-MOS scores.

Conclusions

In this paper, the naturalness of utterances was successfully improved using a stochastic duration predictor and stochastic pitch predictor. Similarly, we significantly improve the naturalness of long, reading-form utterances. In addition, we increase the diversity of generated utterances by integrating a stochastic pitch predictor into Glow-TTS. We show that the distribution of log-F0 values of our model better matches the distribution of log-F0 values of real utterances. This work is important, as it provides a means to generate more realistic and diverse utterances for downstream tasks e.g., data augmentation for automatic speech recognition and long-form audiobooks. It also enables us to perform procedural manipulation of the rhythm and the pitch of generated utterances, given the prior distribution.

Figure 1 :

 1 Figure 1: Proposed Improved Glow-TTS architecture including (a) a Stochastic Duration Predictor and a Stochastic Pitch Predictor, and (b) an Affine coupling layer with pitch and speaker conditioning. WN represents Weight Normalization layers.

Figure 2 :

 2 Figure 2: D-MOS of utterances generated by the evaluated models for both male and female unseen speakers.

Figure 3 :

 3 Figure3: Log-F0 distribution of utterances generated with GlowTTS, GlowTTS-STD, GlowTTS-STDP, and the real VCTK utterances for both male and female speakers. Outliers due to F0 estimation errors have been removed.

We used the official implementation at https://github. com/jaywalnut310/vits.

A stop-gradient operator is applied to the input embeddings when learning the duration and pitch.

https://github.com/wiseman/py-webrtcvad

https://github.com/resemble-ai/Resemblyzer

Speaker embeddings in all of our evaluations were extracted from the fifth utterance (SpeakerID 005) of the VCTK speaker.

pretrained model can be found at https://github.com/ AndreevP/wvmos

Acknowledgements

Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https:// www.grid5000.fr).


Célina Treuillier
email: celina.treuillier@loria.fr

Sylvain Castagnos
email: sylvain.castagnos@loria.fr

Armelle Brun
email: armelle.brun@loria.fr

A MULTI-FACTORIAL ANALYSIS OF POLARIZATION ON SOCIAL MEDIA

Keywords: Polarization Metrics, Social Media, User Behavioral Classes, Opinions, Sources

Polarization is an increasingly worrying phenomenon within social media. Recent work has made it possible to detect and even quantify polarization. Nevertheless, the few existing metrics, although defined in a continuous space, often lead to a unimodal distribution of data once applied to users' interactions, making the distinction between polarized and non-polarized users difficult to draw. Furthermore, each metric relies on a single factor and does not reflect the overall user behavior. Modeling polarization in a single form runs the risk of obscuring inter-individual differences. In this paper, we propose to have a deeper look at polarized online behaviors and to compare individual metrics. We collected about 300K retweets from 1K French users between January and July 2022 on Twitter. Each retweet is related to the highly controversial vaccine debate. Results show that a multi-factorial analysis leads to the identification of distinct and potentially explainable behavioral classes. This finer understanding of behaviors is an essential step to adapt news recommendation strategies so that no user gets locked into an echo chamber or filter bubble.

Introduction

The influence of social media (SM) is tremendously growing worldwide. A recent study from the Pew Research Center1 has shown that one in five adults gets her news primarily through SM, and tends paradoxically to be less well-informed. The impact of SM on polarization can then be explained in two ways. On the one hand, a lack of information due to filter bubbles and echo chambers can affect the degree of polarization of users [START_REF] Azzimonti | Social media networks, fake news, and polarization[END_REF]. On the other hand and paradoxically, the more a polarized user is exposed to opposing views on SM, the more her degree of polarization increases [START_REF] Bail | Exposure to opposing views on social media can increase political polarization[END_REF]. The balance to be found in terms of diversity of opinions, sources, and content is therefore extremely delicate.

Recent studies suggest that it would be more appropriate to tailor the level of diversity and recommendation strategies to behavioral classes [START_REF] Bernstein | Diversity in news recommendations[END_REF][START_REF] Stray | Designing recommender systems to depolarize[END_REF][START_REF] Treuillier | Being diverse is not enough: Rethinking diversity evaluation to meet challenges of news recommender systems[END_REF] rather than to maximize diversity in the same way for all users [START_REF] Helberger | Exposure diversity as a design principle for recommender systems[END_REF][START_REF] Machado Lunardi | A metric for filter bubble measurement in recommender algorithms considering the news domain[END_REF][START_REF] Heitz | Benefits of diverse news recommendations for democracy: A user study[END_REF]. In order to do so, user polarization behaviors need to be understood and modeled in detail. Our work is a first step towards this goal of adapting the recommendations.

Polarization has been investigated in the literature from two different perspectives: a network perspective which mainly relies on the SM structure in order to quantify the polarization of a community and to highlight the content of discussions [START_REF] Badami | Peeking into the other half of the glass: handling polarization in recommender systems[END_REF], and an individual perspective to measure the polarization and the impact of recommender systems through the diversity level of accepted recommendations [START_REF] Möller | Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity[END_REF]. In this paper, we focus on this second perspective, since we aim to quantify the level of polarization at the individual level, and we define 2 research questions. RQ1: Do the current individual polarization metrics contribute to distinguishing polarized users from non-polarized users? RQ2: Can a multi-factorial analysis identify different classes of polarization behavior?

In the rest of this paper, section 2 consists of a literature review about polarization metrics on SM. Section 3 details the experimental setup. The multi-factorial analysis is presented in Section 4, while conclusions and perspectives are drawn in Section 5.

Literature Review

SM play an important role in polarization [START_REF] Kubin | The role of (social) media in political polarization: a systematic review[END_REF][START_REF] Van Bavel | How social media shapes polarization[END_REF]. Conover et al. were the first to study polarization on Twitter [START_REF] Conover | Political polarization on twitter[END_REF]. Since then, many polarization metrics have been put forward and used in the literature, such as modularity [START_REF] Mark Ej Newman | Modularity and community structure in networks[END_REF], controversy [START_REF] Garimella | Quantifying controversy on social media[END_REF], or metrics based on a probability density function [START_REF] Morales | Measuring political polarization: Twitter shows the two sides of venezuela[END_REF]. Though these polarization metrics rely on a varied set of information, they share a characteristic: they quantify overall polarization using a graph representing users and their interactions. However, as well as being explained by social or technological filters, polarization is also influenced by individual factors [START_REF] Geschke | The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers[END_REF] and only a few metrics are designed to quantify individual polarization. Among these individual metrics, we can find the polarization score proposed by Becatti et al. [START_REF] Becatti | Extracting significant signal of news consumption from social networks: the case of twitter in italian political elections[END_REF]. The latter is based on the identification of a set of communities C. The polarization score of a user u depends on the ratio of interactions in each community, with N u,c the number of interactions of u in community c, and N u her total number of interactions. The polarization score ρ(u) of user u corresponds to the maximum of this ratio, as presented in Equation [START_REF] Azzimonti | Social media networks, fake news, and polarization[END_REF].

ρ(u) = max c∈C N u,c N u (1)
Highly polarized users, accessing a unique community have ρ = 1, while users equally accessing all the communities have ρ = 1/C. In our view, this metric has several limits: its minimum bound depends on the number of communities, it only takes into account the community with which the user has interacted the most, and it does not inform about which communities are accessed. In a two-community context, the polarization score presented by Schmidt et al. is similar but ranges between -1 and 1 and is oriented: the value informs about which community is accessed more often [START_REF] Schmidt | Polarization of the vaccination debate on facebook[END_REF].

To go further, Cicchini et al. [START_REF] Cicchini | News sharing on twitter reveals emergent fragmentation of media agenda and persistent polarization[END_REF] propose the Lack of Diversity (LD) metric that is, to some extent, highly similar to the polarization score of Becatti et al. [START_REF] Becatti | Extracting significant signal of news consumption from social networks: the case of twitter in italian political elections[END_REF]. The main difference lies in the fact that it considers sources of information a user interacted with, concretely a set of M media outlets, rather than communities. Each user u is represented by her number of interactions N u,m on news from media m. LD is computed as follows:

LD(u) = max m∈M N u,m • log( |U | |U m | ) ( 2
)
|U | is the total number of users and |U m | is the number of users interacting with media m. The term log( U Um ) corrects a potential bias introduced by m when shared by a large number of users. As in [START_REF] Becatti | Extracting significant signal of news consumption from social networks: the case of twitter in italian political elections[END_REF], LD represents the maximum value within the vector. Thus calculated, the LD metric is not bounded, and should therefore be normalized.

These graph-based metrics, which quantify polarization at the individual level, face a main limit: they are only computed on a single factor (communities or media outlets). However, polarization is known to occur over the influence of multiple factors [START_REF] Chen | Modeling of the public opinion polarization process with the considerations of individual heterogeneity and dynamic conformity[END_REF][START_REF] Cass R Sunstein | The law of group polarization[END_REF]. As a consequence of considering only a single factor, several users may be identified as similarly polarized, but may in fact exhibit a wide range of behaviors and distinguish themselves in different ways.

To summarize, while different metrics have been proposed in the literature, individual polarization metrics are still scarce. Besides, to the extent that they are based on each user's preferred behavior (i.e., the maximum value of the observed variable), we question their ability to accurately differentiate polarization behaviors on SM. This is why we propose to gradually conduct a multi-factorial analysis.

Experimental Setup: Polarization about the Vaccine Debate on Twitter

To answer our research questions (RQ1) and (RQ2), we propose to study a real SM context. We focus on the highly controversial vaccine debate, which was widely discussed following the COVID-19 crisis.

Data Collection

We used the Twitter API (v2), with academic research access. Data collection relies on the concept of elite users [START_REF] Primario | Measuring polarization in twitter enabled in online political conversation: The case of 2016 us presidential election[END_REF] that represent users who are relevant to the subject matter. We assume that elite users' tweets, when related to the selected topic, are always in line with their beliefs. Inspired by the methodology of Primario et al., we fix conditions that elite users must satisfy to ensure their legitimacy: (1) have a significant number of followers; (2) personally manage their Twitter account; (3) are known by the general audience, through media or government interventions; (4) are qualified by education and/or profession to address the subject of matter.

Elite users are an effective entry point for collecting data about a specific topic because their opinions are publicly known [START_REF] Primario | Measuring polarization in twitter enabled in online political conversation: The case of 2016 us presidential election[END_REF]. Nevertheless, as our objective is to analyze standard users' interaction behaviors, it is necessary to have a faithful overview of standard users' interactions about the selected topic during a specific period. The dataset must be balanced in terms of opinion carriers, and representative of behaviors adopted on SM about a specific topic.

To obtain such a dataset, we carried out several steps, run after having chosen the topic, identified a relevant set of elite users, and defined a collection period. These steps are: (1) Collect all tweets published by the set of elite users during the predefined period; (2) Filter tweets about the topic of interest; (3) Collect information about a random subset of interacting standard users for each collected tweet; (4) Identify the most active standard users among those selected in Step 3; (5) Collect all interactions of selected standard users on collected elite users' tweets during the defined period.

Following the procedure detailed above, we manually identified 20 French-speaking elite users having a legitimate voice in the vaccine debate (10 pro-vaccine and 10 anti-vaccine). Their opinion is known because they have clearly expressed it publicly, and the community to which they relate is therefore unambiguous. To preserve their confidentiality and meet Twitter policy, we do not share the names or usernames of the selected accounts. We collected all elite users' tweets between January 1, 2022 and July 31, 2022. Based on relevant vaccine-related hashtags (that we stripped from the tweets) and a random tweet corpus [START_REF] Turenne | The rumour spectrum[END_REF], we trained a two-class classifier based on BertTweetFR [START_REF] Guo | BERTweetFR : Domain adaptation of pre-trained language models for French tweets[END_REF]. This classifier allowed us to keep only elite users' tweets dealing with the vaccination debate. Here, we focus on retweets, which are signs of approval and thus give information about what users agree with [START_REF] Conover | Political polarization on twitter[END_REF]. Thus, we collected information about 100 randomly selected retweeters for each collected tweet, that we hope to be representative of all users. Among the selected retweeters, we focused on the 1,000 most active ones (500 pro-vaccine and 500 anti-vaccine).

Data Analysis

We collected 6,697 tweets in the period, divided into 1,869 tweets from pro-vaccine elite users, and 4,828 tweets from anti-vaccine elite users. From the 1,000 most active retweeters, we collected 11,449,936 retweets. 299,879 of these retweets were on elite users' tweets, with 16,791 retweets on pro-vaccine tweets, and 283,088 retweets on anti-vaccine tweets. This reflects a more intensive activity on the anti-vaccine side, which is consistent with the fact that anti-vaccine supporters are more engaged on Twitter, especially by doing many replies and retweets [START_REF] Germani | The anti-vaccination infodemic on social media: A behavioral analysis[END_REF]. Looking at the structure of the graph, we identify 2 highly connected sets of nodes (modularity=0.55 [START_REF] Clauset | Finding community structure in very large networks[END_REF]), with few edges between them. Besides, the controversy of the vaccine topic, computed using the Random Walk process described by Garimella et al. [START_REF] Garimella | Quantifying controversy on social media[END_REF], is equal to 0.89. This indicates that it is difficult to move from one community to the other one. Altogether, these results first confirm that the selected elite users are tweeting pro-vaccine and anti-vaccine according to the opinion for which they were chosen. Second, they corroborate polarized attitudes towards the vaccination debate within our dataset.

Towards a Multi-factorial analysis of Polarization Behaviors

In this section, we first analyze the information returned by each polarization metric separately, relying on a single factor. Second, we conduct a multi-factor analysis (bi-factor and tri-factor) by considering several metrics. We evaluate the ability to accurately differentiate and characterize polarization behaviors in these different experimental conditions.

A Single Factor Analysis: Metrics from the Literature

To study polarization, we separately study two factors: (1) Opinion factor, where opinions are assessed from the standard users' retweets on each community (pro-or anti-vaccine); (2) Source factor, where sources are assessed from standard users' retweets on each elite user, who act as sources. To quantify polarization on these two factors, we rely on two individual metrics: the polarization score ρ [START_REF] Becatti | Extracting significant signal of news consumption from social networks: the case of twitter in italian political elections[END_REF] and the Lack of Diversity LD [START_REF] Cicchini | News sharing on twitter reveals emergent fragmentation of media agenda and persistent polarization[END_REF] (see Section 2).

Here, as we deal with single factor data, we use Kernel Density Estimation (KDE) rather than well-known multidimensional clustering algorithms to differentiate potential clusters. KDE estimates the probability density function of the studied single factor clusters based on local minima and maxima.

Applied to ρ, the estimated kernel density has no local maxima or minima. This does not allow the differentiation of well-separated clusters. Looking closer at the values of ρ for all standard users, we note that it is not well distributed in [0, 1], and the minimum value is 0.5. This is one of the limits of the metric (See Equation ( 1)), which is bounded in [0.5, 1] in a 2-community context. Users having at least one interaction in each of the communities represent only 18.8% of users, and KDE does not estimate a probability density function allowing to clearly differentiate them. Thus, ρ cannot accurately and finely characterize behaviors depending on whether users interact with 1 or 2 communities. In the same way, the kernel density estimated on LD does not have local minima or maxima as values are distributed continuously. Therefore, this metric does not allow for a differentiation of polarization behavior in terms of access to information sources neither.

A Bi-factor Analysis

In this second step, we rely on a clustering algorithm to identify behavior clusters by combining opinion and source factors. We choose k-means [START_REF] Likas | The global k-means clustering algorithm[END_REF] as it is well suited when dealing with numerical features. The number of clusters k is optimized by maximizing 2 traditional metrics: Davies-Bouldin Index [START_REF] David | A cluster separation measure[END_REF] (the lower the better) and Silhouette Index [START_REF] Peter | Silhouettes: A graphical aid to the interpretation and validation of cluster analysis[END_REF] (the higher the better).

Use of Metrics from the Literature

The optimal number of clusters obtained with ρ and LD factors is k = 2, where Davies-Bouldin Index = 0.60 and Silhouette index = 0.56. Unlike the single factor analysis, considering the two metrics together does allow for the identification of two distinct groups of users, who adopt different polarization behaviors. This bi-factor analysis results in a finer-grained modeling. Identified clusters are shown in Figure 1a. Here again, ρ does not allow a clear differentiation as users interacting with a unique community and those interacting in both communities are not associated with different clusters. Users are clustered according to the LD value, with the two clusters being separated by a threshold fixed around LD = 0.6. Users in the orange cluster C1 with higher values of LD are those with a high polarization according to accessed sources (i.e. retweeted elite users), while the other users (blue cluster C2) retweet more elite users and in a balanced way. However, following the KDE estimation presented in Section 4.1, this threshold is difficult to interpret. Although allowing for the differentiation of two groups of users, which was not possible with a single factor analysis, the limitations identified with respect to the distribution of ρ values and the LD threshold used to delimit the clusters question the quality of the clustering step. We expect the identified clusters to group together users that are likely to adopt well-differentiated polarization behaviors.

Refining Metrics from the Literature: Use of Entropy

As previously mentioned, from our analysis, assessing individual polarization by only considering the predominant opinion (ρ) or source (LD) limits the ability to differentiate between users, and to understand polarization behaviors. To address this limitation, we propose to consider all interactions and represent them as a probability distribution. Following Information Theory, this makes it possible to compute entropy [START_REF] Elwood | A mathematical theory of communication[END_REF]. We thus propose to compute entropy-based metrics, measuring the uncertainty of access to opinions or sources.

Precisely, the more homogeneously distributed the probability mass, the higher the entropy and the greater the uncertainty. As the maximal entropy depends on the number of entities, we propose to use the normalized entropy,

H N (Z) = -n z P (z)log(P (z)) log(n)
, where Z is a discrete random variable that takes n possible values and P (z) is the probability of entity z. For simplicity's sake, from now on, we will refer to normalized entropy as entropy. As entropy is null when there is no randomness and equal to one when the behavior is very heterogeneous, in this work we use H (X) = 1 -H N (X) to get high scores for polarized users. To the best of our knowledge, no individual polarization metrics from the literature rely on it.

We note H op the entropy-based opinion metric, and H so the entropy-based source metric. First, comparing ρ and H op , we can notice that unlike ρ, H op ranges in [0, 1]. Though, as a very large proportion of users only interact with a single community, their polarization on opinion remains maximal, with ρ = H op = 1. The potential contribution of entropy to the differentiation of polarization behaviors will therefore be for the other users (H op = 1), representing 18.8% of standard users. Second, comparing LD and H so , we notice that values are distributed differently. First of all, LD values range in [0.17, 1] while H so values range in [0.09, 1]. Second, the mean of LD values is 0.59, while the mean of H so values is 0.50. To go deeper into this comparison, we analyze the ranked values of LD and H so , and get a Spearman score of 0.92 (p-value= 0). Although relatively high, this score indicates that a significant proportion of users observe large rank variations. Actually, 49.5% of users have a variation higher than 5% of the positions. Given a user u 1 , who has more than half of her interactions with one elite user, LD(u 1 ) = 0.52. Looking at u 1 's other retweets, they are on only 3 other elite users, with two of them having only one retweet. This is reflected in H so (u 1 ) = 0.91, which indicates a high level of polarization. For a significant proportion of users, the information returned by H so is well different from that returned by LD, as for u 1 , and could potentially allow for the identification of different classes of polarization behaviors.

Applying the k-means algorithm on bi-factor data, namely H op and H so , the optimal number of clusters is k = 3, with Davies-Bouldin Index = 0.58 and Silhouette Index = 0.57. Performance is thus close to the one with ρ and LD, but one additional cluster is identified. Clusters are represented in Figure 1b, with orange and blue clusters (C3 and C4) quite similar to the ones identified in Section 4.2.1. The green cluster (C5), corresponds to a subset of 24 users with lower H op values, thus interacting with both communities. In an unprecedented way, entropy-based metrics thus allow differentiating standard users both on opinion and source factors, which was not the case with ρ and LD. More importantly, clustering on H op and H so contributes to identifying a new subset of users, interacting with both communities and potentially acting as intermediates between pro-vaccine and anti-vaccine users. The resulting clusters thus provide useful observations about the polarization of users on SM. A bi-factor analysis, coupled with the use of entropy-based metrics, has made it possible to differentiate new classes of behavior. We now wish to assess to what extent additional factors could further improve the quality of the polarization behaviors modeling.

A Tri-factor Analysis

One main limit of the literature and of the previous sections is that the metrics evaluate to what extent users are polarized, but do not inform towards which community. We assume that this could improve the clustering.

In this respect, and considering the opinion metric, we propose to apply a transformation factor, as follows:

H ± op = ±H op + 1 2 (3)
The plus-minus sign in front of H op depends on the predominant community. We set H op > 0 if interactions are in favor of the pro-vaccine community, and H op < 0 otherwise. The final H ± op values range in [0, 1], with H ± op = 0 indicating a very high polarization in the anti-vaccine community, H ± op = 1, corresponding to an extreme polarisation in the pro-vaccine community and H ± op = 0.5 reflecting balanced interactions between the two communities. Considering the source metric, and still to inform about each community, we propose to split it into two metrics. The entropy-based metric H so , which initially combined sources from both communities, is split into two metrics H so,pro and H so,anti , corresponding to the source factor computed on either pro or anti-vaccine community. This is designed to better differentiate users with unbalanced polarization in each of the two communities.

The optimization of k-means algorithm on the tri-factor data indicates that the optimal value of k is 4, with Davies-Bouldin Index = 0.51 and Silhouette Index = 0.74. Performance is thus significantly higher than with two factors.

Looking at Figure 1c, representing identified clusters, we see that the clusters identified are quite different from those described in Section 4.2. First, blue and red clusters (C6 and C7) respectively correspond to highly polarized anti-vaccine users and pro-vaccine users. These users, accessing a unique community, do not all behave the same way according to the source factor as values are distributed in [0, 1]. Besides, the yellow and pink clusters (C8 and C9) are of particular interest. Looking more closely at the opinion and source metrics within these clusters, users in the yellow cluster (C8) are those having H ± op values close to 0.5, indicating a balanced activity between the two communities. In each accessed community, these users interact with a variety of sources, as H so,pro and H so,anti values are evenly distributed among users. Besides, users associated with the pink cluster (C9) interact predominantly with the anti-vaccine community. Nevertheless, they all have at least 1 interaction in the pro-vaccine community, in which they mostly interact with a few elite users (H so,pro ≈ 1). Users taking part in these two unprecedented clusters are intermediate users as they interact in both communities. Moreover, in addition to being quite different from those identified in Section 4.2.2, they are also much more numerous. Yellow and pink clusters (C8 and C9) contain 43 and 140 standard users respectively, while only 24 intermediate users were previously identified. It appears that a significant proportion of users do not engage in extreme polarizing behaviors.

Overall, the identification of four patterns of polarization, only possible with the last analysis, is very interesting and reflects the multi-factorial nature of polarization. Identified behavioral classes are well-separated according to specific characteristics, which was not the case when considering only one or two factors on traditional or entropy-based metrics (Sections 4.1 and 4.2). Nevertheless, a few users are at the intersection between clusters, whose membership to one or other of the groups is questionable. For example, some users associated with the blue cluster C6 (i.e. highly polarized users in the anti-vaccine debate community) are also very close to the pink cluster C9. These users may be in a transitional phase which might be of interest.

Conclusion and Perspectives

The literature still lacks individual polarization metrics allowing fine-grained modeling of users' polarization behaviors.

As the latter are the result of multiple influences, we conducted a multi-factorial analysis of polarization. Experiments confirm, first, that metrics from the literature using maximum value are too restrictive, and that the proposed entropybased metrics allow a finer distinction between polarization behaviors, both for opinion and source factors. It does indeed contribute to the identification of an additional cluster of under-represented users, who have retweet interactions in both communities. These users adopt moderately polarized behavior about a highly polarized topic on SM. Altogether, these results indicate that current polarization metrics do not distinguish polarization behaviors properly (RQ1), and that entropy-based metrics seem better adapted. Besides, conducting a tri-factor analysis allows an unprecedented identification of well-separated behavioral clusters, which emphasizes that an adequate combination of factors leads to more reliable modeling of polarization behaviors (RQ2).

In a process of opening the filter bubbles, and reducing the polarization phenomenon, such a multi-factorial analysis could be greatly beneficial. In a strongly polarized context, within which users have formed strong opinions, an input of diverse items does not always have the desired effect. In fact, providing diversity can be tricky due to the strong opinions held by users, who are potentially very wary of being exposed to contrary ideas. It may even reinforce the polarization phenomenon [START_REF] Bail | Exposure to opposing views on social media can increase political polarization[END_REF]. An accurate characterization of adopted polarization behaviors could help to adapt solutions, including through recommendations. Each identified behavioral class could benefit from different recommendation strategies.

To go further, the intermediate classes of users identified through the multi-factorial analysis could help to gradually expose highly polarized users to different viewpoints. This could limit the potential adverse effects of diversity, and help build trust-based recommendations. Especially, users whose membership to one unique cluster is uncertain could serve as bridges between the different classes close to them. The future development of depolarizing recommender systems could probably rely on a multi-factorial analysis of polarization to limit this worrying phenomenon.

  H op and H so .

  H ± op , H so,pro and H so,anti

Figure 1 :

 1 Figure 1: Clusters identified with bi-factor data (3a and 3b) and tri-factor data (3c).

https://www.pewresearch.org/journalism/2020/07/30/americans-who-mainly-get-their-news-on-social-media-are-lessengaged-less-knowledgeable/


Hugo Scurto
email: hugo.scurto@ensad.fr

Axel Chemla-Romeu-Santos

Deeply Listening Through/Out the Deepscape

Keywords: Deep Learning, Deep Listening, Diffractive Art Practice

This paper presents early artistic, conceptual and technical work toward practicing and theorising through/out the deepscape. I first introduce the concept of deepscape, which may designate the global flows of media intensively computed by deep learning throughout the Internet, entangled with the material, human and cultural resources they capitalise on throughout corporate infrastructures of artificial intelligence (AI). I then propose to explore deep listening of soundscapes generated by deep learning as a practice to raise awareness of the planetary scale of the deepscape. I relate the diffractive prototyping of a deep generative model of soundscape, based on the multichannel hacking of the Realtime Audio Variational au-toEncoder (RAVE), trained on worldwide soundscape data that I transversally recorded over 28 places in late April 2022, using the Locustream online sound map. I argue that listening to the planetary soundscape that continually flows from this deep generative model may reveal the ethico-onto-epistem-ology of deep learning, by recalling the landscapes that are being exploited by infrastructures of AI, while situating data collection practices and training costs of deep learning. The paper ends by discussing art and science work that might be engaged to reveal and reconfigure the deepscape in depth.

Introduction

For over half a century, a vast majority of scientists have been developing and describing Artificial Intelligence (AI) through anthropomorphic conceptions and representations. From symbolic AI to modern Artificial General Intelligence [START_REF] Goertzel | Human-level artificial general intelligence and the possibility of a technological singularity: A reaction to ray kurzweil's the singularity is near, and mcdermott's critique of kurzweil[END_REF], simulating human-like intelligence through quantification and computation have been standard goals and methods for most researchers in the field. These anthropomorphic conceptions stem from millenary Western representations of AI as humanoid robots, or artificial objects endowed with human intelligence [START_REF] Mccorduck | History of artificial intelligence[END_REF], which are still actively shaping popular representations, along with corporate narratives, on AI [START_REF]Better Images of AI[END_REF].

The technological revolution brought by deep learning in the last decade paradoxically destabilised anthropomorphic conceptions of AI [START_REF] Bory | Deep new: The shifting narratives of artificial intelligence from deep blue to alphago[END_REF], while simultaneously reinforcing anthropocentrism. On the one hand, difficulties in interpreting internal representations of deep neural networks, as well as images they can generate [START_REF] Hertzmann | Visual indeterminacy in gan art[END_REF], pushed scientists and artists to approach AI as a form of non-human intelligence [START_REF] Korteling | Human-versus artificial intelligence[END_REF][START_REF] Akten | Deep meditations: Controlled navigation of latent space[END_REF]. On the other hand, systems powered by deep learning, from facial recognisers to text and image generators [START_REF] Ramesh | Zero-shot text-to-image generation[END_REF][START_REF] Brown | Language models are fewshot learners[END_REF], entailed worldwide AI applications, both public and private, across almost all sectors of human society [START_REF] Parloff | From 2016: Why deep learning is suddenly changing your life[END_REF].

As an artist-researcher, I am interested in developing a nonanthropocentric representation of AI that raises awareness of the planetary costs of deep learning. I am inspired by Kate Crawford and Vladan Joler's Anatomy of an AI system (2018) [START_REF] Crawford | Anatomy of an ai system[END_REF], whose large-scale map reveals the globalised, extractive infrastructures that lie beneath modern AI applications, and that are barely tackled by anthropocentric discourses. I am also interested in engaging in diffractive art practice with deep learning [START_REF] Scurto | Prototyping machine learning through diffractive art practice[END_REF] to perform and reconfigure this nonanthropocentric representation. I am inspired by Memo Akten's Deep Meditations (2018) [START_REF] Akten | Deep meditations: Controlled navigation of latent space[END_REF], whose deep learninggenerated continual flows of images and sounds let us embrace the interconnectedness of AI with all socio-material bodies of time and space, including us, and the planet.

In this paper, I introduce the concept of deepscape, along with deep listening experiences of planetary soundscape livegenerated by one deep neural network. The deepscape designates the global flows of deep learning-generated media intensively computed by deep learning throughout the Internet, entangled with the material, cultural and human resources they capitalise on throughout corporate infrastructures of AI. Deep listening experiences [START_REF] Oliveros | Deep listening: A composer's sound practice[END_REF] of deep learning-generated continual flow of soundscape aim at raising awareness of the deepscape, by leveraging a planetary representation for a deep neural network, and recalling the landscapes that are being exploited by infrastructures of AI. By including nonhuman voices and planetary scales within research and practice with deep learning, this research-creation thus seeks to resist the anthropocentric culture of modern AI research, illuminating its media aesthetics and socio-ecological impacts.

The next sections successively describe an early conceptual mapping of the deepscape, the diffractive prototyping of a deep generative model of planetary soundscape, and deep listening experiences led through/out the deepscape. The paper ends by discussing art and science work that might be engaged to reveal and reconfigure the deepscape in depth.

Mapping the Deepscape

In this section, I sketch an early map of the deepscape, by describing the global media flows, corporate computational infrastructures, and planetary socio-material resources that compose it. I argue that the concept of deepscape better represents the media aesthetics and planetary scales of deep learning than mainstream anthropomorphic conceptions and representations of AI. Furthermore, I suggest that the deepscape may inspire new practices with deep learning, which may complement those with/in AI, as we will see.

Global Media Flows

Since DeepDream almost a decade ago [START_REF] Mordvintsev | Inceptionism: Going deeper into neural networks[END_REF], the Internet has been flooded by media generated by deep learning. These media encompass image, sound and text, and more recently, video, 3D movement or biosignals [START_REF] Scurto | Ganspire: Generating breathing waveforms for art-health applications[END_REF]. Sounds and images generated by deep learning also permeate our physical environments, with voices of AI assistants resonating in smart homes and cities, and mobile-based face filters re/acting over our bodies across the globe [START_REF] Javornik | what lies behind the filter?'uncovering the motivations for using augmented reality (ar) face filters on social media and their effect on well-being[END_REF]. The concept of deep fake has made popular such media across fiction and reality, raising awareness of deep learning's threats to steal one's identity [START_REF] Boylan | Will deep-fake technology destroy democracy[END_REF]. As AI-based virtual worlds are rapidly evolving, we can expect these media flows to grow in the near future [START_REF]What if 99% of the Metaverse is generated by AI?[END_REF].

Taking inspiration from Arjun Appadurai's notion of scape [START_REF] Appadurai | Modernity at large: Cultural dimensions of globalization[END_REF], I propose to introduce the notion of deepscape to point to these global flows of media intensively computed by deep learning and permeating digital and physical worlds. Rather than fake, I argue that these media are real, based on the impact they have on cultures and societies, and the planetary computation they build on to exist, as we will see. Importantly, such media are materially configured to exist under the form of generative flows, due to computational architectures of deep learning, which can either support streamable media generation [START_REF] Caillon | Streamable neural audio synthesis with non-causal convolutions[END_REF], or continuous interpolation between images or sounds [START_REF] Arvanitidis | Latent space oddity: on the curvature of deep generative models[END_REF]. Artists have well explored these material attributes of deep learning in stream-based installations [START_REF] Audry | Art in the Age of Machine Learning[END_REF], including dadaBots and their infinite online music livestreams [START_REF] Carr | Generating albums with samplernn to imitate metal, rock, and punk bands[END_REF], or Anna Ridler and her moving images [START_REF] Ridler | Mechanized cacophonies[END_REF].

While humanoid robots surrounded by blue zeros and ones keep on being summoned to promote modern AI applications [START_REF]Better Images of AI[END_REF], I suggest that the singular aesthetics of media flows generated by deep learning are starting to replace anthropomorphic representations of AI at a global scale in our collective imagination. Indeed, ambiguous attributes of images generated by GANs [START_REF] Hertzmann | Visual indeterminacy in gan art[END_REF] (Generative Adversarial Networks, a specific type of deep neural network), along with the almost infinite interpolative flows from which they emerge, have permeated our representations of machines across art and science, along with our conception of how they may exhibit creativity [START_REF] Grba | Deep else: A critical framework for ai art[END_REF]. In this paper, I limit my analysis to witnessing such global flow aesthetics of media generated by deep learning, and how they are transforming our representations of AI.

Corporate Computational Infrastructures

Different types of computational infrastructures actually support the deepscape and its global media flows. The first is the Internet [START_REF] Chatonsky | Esthétique des flux (après le numérique)[END_REF], since media generated by deep learning actively flows through social networks [START_REF]What if 99% of the Metaverse is generated by AI?[END_REF], helped by mobile applications. A second type is deep generative models [START_REF] Foster | Generative deep learning: teaching machines to paint, write, compose, and play[END_REF], which are compressed representations of datasets, computed by deep learning, enabling stream-like generation of media that more or less resemble the dataset. A third type is supercomputers and data centers combined with cloud computing [START_REF] Strubell | Energy and policy considerations for deep learning in nlp[END_REF], since deep learning requires intensive computation and large amounts of data to produce deep generative models.

Over the last decade, such computational infrastructures have been in majority invested, developed and owned by global corporations, including the Big Tech. In addition to owning Instagram and Facebook, Meta recently introduced their AI Research Supercluster, consisting of 16,000 graphical processing units (GPUs) especially designed to develop deep learning applications, parallel to their metaverse [START_REF]Introducing the AI Research SuperCluster[END_REF]. OpenAI-recently followed by Meta, Google and Microsoft-developed several popular deep generative models, including Dall-E [START_REF] Ramesh | Zero-shot text-to-image generation[END_REF], GPT-3 [START_REF] Brown | Language models are fewshot learners[END_REF] and Jukebox [START_REF] Dhariwal | Jukebox: A generative model for music[END_REF], enabling to generate image, text and music respectively, based on text prompts. Google developed Colaboratory [START_REF] Carneiro | Performance analysis of google colaboratory as a tool for accelerating deep learning applications[END_REF], which leveraged the corporation's GPU-based cloud computing services to accelerate deep learning applications, in turn enabling the emergence of a global community of practice dedicated to deep generative models [START_REF] Oppenlaender | The creativity of text-to-image generation[END_REF]. Importantly, Google also develops and maintains TensorFlow, one of the most widely used library to develop deep generative models and modern AI applications [START_REF] Abadi | Tensorflow: a system for large-scale machine learning[END_REF].

I suggest that corporate computational infrastructures are going to exponentially increase the scale of the deepscape and its global media flows in the next coming years. In addition to the Big Tech, hundreds of worldwide corporations have been investing in deep generative models recently, constituting what have been called a "generative AI landscape" or "creative new world" by investors and analysts [START_REF]Generative AI: A Creative New World[END_REF]. It is highly probable that these global corporate investments are to modify distribution of work in the coming years, especially in the creative and cultural industries [START_REF] Amato | Ai in the media and creative industries[END_REF]. Many artists have made such political aspects of AI central to their practice with deep learning [START_REF] Caramiaux | explorers of unknown planets": Practices and politics of artificial intelligence in visual arts[END_REF]. Holly Herndon and Mathew Dryhurst proposed artist-led platforms addressing legal issues related to ownership of training datasets [START_REF]Spawning[END_REF], and of deep generative models themselves [START_REF] Herndon | Holly+[END_REF]. In this paper, I limit my analysis to pointing out to corporate computational infrastructures that support the deepscape, and how they may generate governmental issues at a planetary scale.

Planetary Socio-Material Resources

Last but not least, a wide array of planetary resources enables to fuel the deepscape, exploited by corporate computational infrastructures. A first type is material resources [START_REF] Crawford | Anatomy of an ai system[END_REF][START_REF] Parikka | A geology of media[END_REF]. Rare elements such as lithium are required to build the supercomputers enabling deep learning computations, along with personal computers and smartphones required to interact with deep generative models [START_REF] Heelan | Current and prospective li-ion battery recycling and recovery processes[END_REF]. Extracting such material elements devastate landscapes across the globe, while contributing to huge levels of atmospheric and water pollution, which in turn generate political conflicts between local populations, opaque intermediaries and global corporations [START_REF] Crawford | Atlas of AI[END_REF]. In addition, electricity consumption of deep generative models breaks records and should continue increasing every year, de-spite engineering efforts to reduce computational costs [START_REF] Strubell | Energy and policy considerations for deep learning in nlp[END_REF]. This leads to huge carbon emissions that threaten the lives of both humans and non-humans across the globe.

A second type is human resources. Workers are required to extract material resources needed to build computational infrastructures of deep learning, often enduring awful economic and health conditions [START_REF] Crawford | Atlas of AI[END_REF]. Large teams of engineers actively maintain such computational infrastructures [START_REF] Abadi | Tensorflow: a system for large-scale machine learning[END_REF], from deep learning libraries to mobile AI applications, across various hardware and software, as computational technology keeps on rapidly evolving. Computer scientists are required to design and implement deep generative models. While they sometimes build and collect the datasets over which they train their models, they essentially delegate data work to disadvantaged people across the globe [START_REF] Gray | Ghost work: How to stop Silicon Valley from building a new global underclass[END_REF], thus reinstantiating work inequalities and data colonialism [START_REF] Couldry | Data colonialism: Rethinking big data's relation to the contemporary subject[END_REF] through deep learning. Far from objective, deep generative models thus perpetuate values and world views of a few [START_REF] Scheuerman | Do datasets have politics? disciplinary values in computer vision dataset development[END_REF], reinforcing cultural bias of the global computational infrastructure.

Indeed, a third type of resource that directly relates to this data work is cultural resources. Beyond mere numbers, datasets carry historical and cultural value, as images, sounds or text they represent are always and already entangled within communities that produced it, and labels and classifications they encapsulate reflect certain world views [START_REF] Scheuerman | Do datasets have politics? disciplinary values in computer vision dataset development[END_REF]. When a person or a corporation trains a deep generative model over a given dataset, it exploits its cultural value to generate media that somehow reproduces this value, while distorting or even biasing it in unethical ways [START_REF] Srinivasan | Biases in generative art: A causal look from the lens of art history[END_REF]. Artists such as Stephanie Dinkins have sought to illuminate responsible ways of dealing with datasets and deep generative models, especially toward communities that are underrepresented in deep learning, such as the Black community [START_REF] Dinkins | Community, art and the vernacular in technological ecosystems[END_REF]. Cultural accountability may eventually become difficult to tackle, as corporate AI infrastructures have started using datasets produced by deep generative models to train deep generative models [START_REF] Ramesh | Zero-shot text-to-image generation[END_REF]. In this paper, I limit my analysis to pointing out to socio-material resources that fuel the deepscape, and how they damage socio-ecological landscapes of the planet.

Diffractive Deep Learning Prototyping

In this section, I describe the diffractive prototyping of a deep generative model of planetary soundscape, based on a transversal soundscape dataset that I collected over the globe in one week-end, and the multichannel hacking of the RAVE variational auto-encoder [START_REF] Caillon | Rave: A variational autoencoder for fast and high-quality neural audio synthesis[END_REF], made in collaboration with the second author. I argue that such a diffractive practice with deep learning reveals and reconfigures the deepscape, by performing the planetary scale of deep learning, while illuminating the material costs that its computations imply.

Transversal Soundscape Dataset

I identified three requirements for my data collection practice to engage with the concept of deepscape. First, data should be collected across worldwide places to transcribe the planetary scale of deep learning. Second, data should be in the form of multichannel soundscapes to convey a sense of spatial landscape through sound. Third, data should combine anthropophony, biophony and geophony [START_REF] Krause | Anatomy of the soundscape: evolving perspectives[END_REF] to have the model embody a sonic environment rather than one individual form of intelligence, such as a musician or a bird.

I used Locustream to collect this soundscape dataset [START_REF] Sinclair | Locus stream open microphone project[END_REF]. Locustream is an online sound map developed by the Locus Sonus research group that enables to listen to live stereophonic soundscapes across the globe. Practitioners can setup their own microphone in the place of their choice, and livestream the sound it captures online, using either a Pure-Data patch running on a RaspberryPi, or a mobile application for smartphone. The map has been used over sixteen years by artists and scientists to promote novel forms of listening to, and performing with, the planet and its diverse environment.

I decided to lead online field recording through the Locustream online sound map. Specifically, I opted to restrain my field recording to one local portion of time-namely, one week-end in late April 2022-, while widening it to all portions of the globe accessible on the map over that periodnamely, twenty-eight places across seventeen countries and five continents. I called it transversal, following scientific dataset nomenclatures used in statistics. On the one hand, such a transversal dataset let me situate ecological properties of soundscapes related to planetary seasonal shifts, be they related to human or non-human activities. On the other hand, it enabled me to materialise the planetary scale of deep learning, since all places, cultures and species of the transversal dataset would be eventually normalised and approximated as one planetary soundscape by deep learning.

The resulting dataset consists in nearly 16 hours of stereophonic soundscape (see Figure 1), that I have deeply listened to while recording, with a mean duration of 31 minutes per location (see Figure 2). Such small data approach come within the scope of diffractive art practice [START_REF] Scurto | Prototyping machine learning through diffractive art practice[END_REF].

Deep Generative Model

Along with the second author, we opted to work with the Realtime Audio Variational autoEncoder (RAVE) [START_REF] Caillon | Rave: A variational autoencoder for fast and high-quality neural audio synthesis[END_REF]. RAVE is a state-of-the-art deep generative model that learns to generate raw audio waveforms using a two-stage training procedure. In the first stage, a variational auto-encoder is trained to learn a high-level latent space of the training dataset. In the second stage, only the decoder is trained with an adversarial generation objective to enforce continuity of the latent space. One advantage of RAVE over other deep generative models are that audio signals are modeled and generated at a 48 kHz sampling frequency, i.e., standard audio quality.

The second author hacked RAVE's implementation1 by adding a multichannel audio loader, which enables to load audio data with c channels directly for use in RAVE, including the multichannel factor c into encoder, decoder, and adversarial parameters. From a methodological perspective, this add-on could be described as a form of learnable algorithm, which comes within the frame of diffractive art practice [START_REF] Scurto | Prototyping machine learning through diffractive art practice[END_REF]. The source code for our modified implementation will soon be publicly available. Our deep generative model can be used with the nn∼ object for neural audio synthesis 2 .

Training and Streaming Costs

We trained our deep generative model on our transversal soundscape dataset for three million steps, specifically two and one million steps for first and second stages. Trainings were conducted using a private infrastructure, which has a carbon efficiency of 0.432 kgCO 2 eq/kWh. A cumulative of 360 hours of computation was performed on GPU hardware of type Titan V (TDP of 250W). Total emissions are estimated to be 38.88 kgCO 2 eq of which 0 percents were directly offset. These estimations were conducted using the MachineLearning Impact calculator presented in [START_REF] Lacoste | Quantifying the carbon emissions of machine learning[END_REF]. Streaming costs are equivalent to working with a personal computer, as RAVE support streamable audio generation on standard CPUs [START_REF] Caillon | Streamable neural audio synthesis with non-causal convolutions[END_REF].

We used the same model hyperparameters and architecture as provided in the original RAVE implementation [START_REF] Caillon | Rave: A variational autoencoder for fast and high-quality neural audio synthesis[END_REF]. Specifically, we preprocessed our transversal soundscape dataset by cropping it into one-second audio slices to apply dequantization, random crop and all-pass filters with random coefficients as data augmentation strategy. Training evaluation was led by combining quantitative and qualitative dimensions of sound generation, with the first training stage stopped based on joint quantitative observation of loss minimisation and qualitative listening of generated audio, and the second stage stopped based on qualitative listening criteria.

Deep Listening Experiences

In this section, I relate first deep listening experiences created through/out our deep generative model of planetary soundscape. I group them within an ongoing artwork called Deepscape: Transversal. Deep listening is a meditative aesthetic proposed by composer Pauline Oliveros "to inspire both trained and untrained performers to practice the art of listening and responding to environmental conditions in solo and ensemble situations" [START_REF] Oliveros | Deep listening: A composer's sound practice[END_REF]. I suggest that deep listening may be helpful to raise awareness of the deepscape and its interconnectedness with all socio-material bodies of the planet.

The following paragraphs describe online radiophonic broadcast, collective acousmatic listening, and open listening and discussion situations, inviting diverse audiences to experience planetary soundscape flows computed by our deep generative model, and describe their spatial, temporal and material attributes. These situational wholes enabled to illuminate somaesthetic behaviours of our deep generative model, which jointly come within the scope of diffractive art practice [START_REF] Scurto | Prototyping machine learning through diffractive art practice[END_REF].

Online Radiophonic Broadcast

The first experience consists in the online radiophonic broadcasting of stereophonic audio live-streamed by our deep generative model. It is mounted on the Locustream online sound map 3 since October 17th, 2022, while being temporarily stopped at times. On the server side, I used the nn∼ Max/MSP object for neural audio synthesis, and the Locus Sonus Locustream Pure Data patch [START_REF] Sinclair | Locus stream open microphone project[END_REF] to mount the live stream online. On the deep learning side, I experimented with RAVE prior for unconditional audio generation [START_REF] Caillon | Streamable neural audio synthesis with non-causal convolutions[END_REF]. On the sound map, the stream is located at the material place where the deepscape is computed, that is, from my home as of now. To inspire deep listening, the broadcast is accompanied with a note of intent that include two questions: Who terraforms this deepscape? Whose scapes are getting threatened as AI sucks our attention away from planetary issues?

Collective Acousmatic Listening

The second experience consisted in a collective acousmatic listening session led at Cirque Électrique, Paris, France, on November 5th, 2022, for the opening of an AI-based experimental music concert. It consisted in 15 minutes of planetary soundscape 4 , recorded in one shot from our deep generative model, based on real-time reconstruction of stereophonic audio coming from a microphone placed at my home's window. The latter was picking up a flow of environmental soundscape, which included keyboard sounds, human sounds and discussions, bird vocalisations, car engines, wind howlings and children screaming in their playground.

The session started by an on-stage presentation of the piece, as well as listening guidance standing as deep listening instructions. Specifically, I told the audience that they were going to be put in the dark to listen to 15 minutes of soundscape generated by one deep learning model; that they may approach this experience as sound art rather than music, and that they may let themselves immerse into imaginary worlds while listening. As we set the volume relatively high to produce strong bass, I warned the audience that the experience was going to be quite loud, and therefore, that they should not hesitate to leave if it became difficult.

The audience-approximately one hundred people-was standing up while put in the dark for acousmatic listening. During listening, several people were witnessed closing their eyes and collectively relaxing their necks and bodies while standing up. At the end of the session, some audience members reported how the listening experience felt shorter than 15 minutes, which somehow testifies of their immersion in deep listening. They also felt a sense of space while listening throughout the stereophonic stream. They reported encountering non-human voices throughout the experience, such as humid rocks, strong wind, screaming dogs and singing birds.

Open Listening and Discussion

The third type of experience consisted in open listening and discussion sessions led with art-research practitioners, teachers and students. The first was a workshop at Kallio-Kuninkala, Järvenpää, Finland, on August 24th, 2022. The second was a public event at École d'Art d'Aix-en-Provence, France, on October 9th, 2022. The third was a seminar at École des Beaux-Arts de Paris, France, on November 7th, 2022. Sound materials included both unconditional audio live-streamed by our deep generative model, and the 15minute planetary soundscape previously recorded.

The sessions started with conceptual elements about the deepscape, as well as technical information on how we prototyped our deep generative model. Then, I communicated listening guidance standing as deep listening instructions. Specifically, I told practitioners and students to listen attentively to the stereophonic soundscape flowing from our deep generative model, and ask themselves the following three questions: Where am I? What am I listening to? To whom does it cost? In addition, I asked them to imagine five words that would best describe their listening experience. Due to the openness of these sessions, most participants did not strictly follow these instructions, but rather used them as listening guidance to foster their imagination.

The conceptualisation of the deepscape found echo in most art-research practitioners and teachers. Participants that involved in the discussion generated various interpretations of the deepscape through/out listening. One artist had the feeling to be "on another planet inhabited by Martians". One composer described "an impossible landscape of windy ocean with small insects and land grass". Rather than a soundscape, one teacher felt they were under a "strange living creature", or "monster", attempting to regurgitate things with its throat. Students felt they witnessed "noises of a diver", moving "inside and outside water" while "adjusting their oxygen level". One of them added: "It's a good profile of what a planet is sounding like. It's like a blue planet."

Discussion

In this section, I discuss further art and science work to be done to reveal and reconfigure the deepscape in more depth. Specifically, I discuss the planetary media computation driven by AI and revealed by the deepscape, the inseparability of ethics, ontology and epistemology when engaging with deep learning, and current work led toward bodily intra-acting through/out the deepscape.

Deepscape as Planetary Media Computation

Discourses on AI almost always suggest anthropomorphic conceptualisations and representations among expert and novice audiences. These conceptualisations often dismiss the media aesthetics of deep learning, as its computed flows of images, sounds and texts intensively permeate worldwide physical and digital environments. By evoking a sense of spatiality with the suffix "-scape", and of deep learning with the prefix "deep-", the concept of deepscape may reveal the planetary media computation driven by AI, recalling the intensive socio-material extractions led by corporate infrastructures to support its media flows across the planet [START_REF] Parikka | A geology of media[END_REF]. I am looking forward to critiques from other researchers to reconfigure this concept in more depth and entangle a plurality of perspectives and disciplines within a potential theoretical framework.

From a practical perspective, the concept of deepscape also emphasises the landscape aesthetics that accidentally emerged from deep learning. Indeed, first art practices with deep learning consisted in navigating so-called latent spaces computed by deep learning over scientific datasets built for image generation [START_REF] Caramiaux | explorers of unknown planets": Practices and politics of artificial intelligence in visual arts[END_REF]. Rather than the realism originally sought by scientists, the most intriguing findings essentially lied in ambiguous images, that deep learning interpolated or extrapolated from the dataset [START_REF] Hertzmann | Visual indeterminacy in gan art[END_REF]. Artists then curated and showcased such findings as art, or sought to recreate this navigation process in art installations, typically presenting the audience with deep learning-generated flows of sounds and images [START_REF] Audry | Art in the Age of Machine Learning[END_REF]. I suggest that this process is reminiscent of field recording, where sounds and images are experienced in a continuous flow, while navigating unknown places, sometimes producing strange encounters that exceed human interpretation skills, and careful data collection that might reveal the interconnectedness of the recorder with the planet [START_REF] Krause | Anatomy of the soundscape: evolving perspectives[END_REF].

Recently, artists have been exploring spatial concepts for AI, describing it as "imaginary landscapes" [START_REF] Tokui | Imaginary soundwalk[END_REF] conveying a "worldview" through their training data [START_REF] Akten | Learning to see: you are what you see[END_REF], or even crafting deep generative models to create virtual places [START_REF] Ridler | Mechanized cacophonies[END_REF]. My Deepscape: Transversal artwork might contribute to these works, by situating data collection practices and planetary costs of deep learning within art practice, while seeking to resist the anthropomorphic culture of modern AI research.

Ethico-onto-epistem-ology of Deep Learning

Our diffractive prototyping of deep learning illuminates the inseparability of ethics, ontology and epistemology when engaging with deep learning. As we discussed above, collecting a dataset entails an ethics of ownership, representativeness and positionality. In our case, all soundscapes recorded online were open sourced by the Locustream online sound map [START_REF] Sinclair | Locus stream open microphone project[END_REF]. Specifically, each soundscape comes with a complex history of negotiating with local populations and authorities, ensuring that an open microphone would hinder human and non-human communities as little as possible. Furthermore, the transversal soundscape dataset pragmatically represents socio-economic abilities of worldwide populations and countries to buy microphones and provide access to the Internet. Lastly, my positionality was described through the relying on a transversal data collection method, of which I attempted to be transparent on the advantages, motivations and limitations to it, as well as through my choice to deeply listen to one stream at a time while recording, instead of automatically recording all streams simultaneously using machine listening.

I suggest that dataset building inevitably produces an ontology of deep learning-generated media. Reactions harvested along the three deep listening experiences testify of the planetary soundscape flowing from our deep generative model, approximating and normalising worldwide soundscapes in the dataset. Making training and streaming costs transparent through our technical collaboration, as suggested by diffractive art practice [START_REF] Scurto | Prototyping machine learning through diffractive art practice[END_REF], also illuminates the material entanglement of such planetary soundscape with planetary resources exploited by infrastructures of AI to produce deep generative models. Beyond artwork analysis [START_REF] Audry | Art in the Age of Machine Learning[END_REF][START_REF] Caramiaux | explorers of unknown planets": Practices and politics of artificial intelligence in visual arts[END_REF][START_REF] Grba | Deep else: A critical framework for ai art[END_REF], I suggest that further research might be done on corporate deep generative models, such as Dall-E, GPT-3 or Jukebox, to reveal the ontologies they produce, since they are often trained unethically over large datasets automatically scraped from the Internet.

Lastly, deep listening experiences of my Deepscape: Transversal artwork may also promote a alternative epistemology for deep generative models. Indeed, scientists often evaluate deep generative models through fast and quantitative methods, typically automatically-generating short samples with deep learning, and measuring reconstruction quality using Likert scales [START_REF] Caillon | Rave: A variational autoencoder for fast and high-quality neural audio synthesis[END_REF]. I suggest that approaching media flows computed by deep learning through slower and careful crafting may reconfigure how we conceptualise deep generative models, and perhaps, how we develop them. While such crafting practices are emerging naturally among communities of practices dedicated to deep generative models [START_REF] Oppenlaender | The creativity of text-to-image generation[END_REF], I suggest that researchers also have a responsibility to promote such slower ways of producing knowledge about deep generative models, in a rapidly-evolving computational society.

Bodily Intra-Acting Through/Out the Deepscape While deep listening may help grasp the planetary scale of the deepscape, it might remain opaque for certain audiences that are not accustomed to music or sound art practices. Fortunately, there remains several ways of practicing and bodily intra-acting [START_REF] Scurto | Prototyping machine learning through diffractive art practice[END_REF] through/out the deepscape. Along with other artists [START_REF] Tokui | Imaginary soundwalk[END_REF][START_REF] Ridler | Mechanized cacophonies[END_REF], I am currently exploring landscape image generation with deep learning. While images remain twodimensional, they might recall the landscapes of the planet that are getting damaged as the deepscape rapidly evolves.

I am also currently leading sound and image field recording in one local portion of space, while widening it to all portions of time accessible over one year. Such a longitudinal data collection practice should complement the transversal method presented in this paper. Instead of a planetary soundscape, it may enable to learn a model of the planet itself, as sound and light recorded over the year from one identical place may reflect the planet's motion in time and space, with corresponding climatic variations. Such a deep longitudinal model should further my diffractive art practice with deep learning, revealing further ecological entanglements of AI with all socio-material bodies of the universe.

Lastly, I am currently exploring soundwalk-inspired modes of listening to, and practicing with, deep learning. I am collaborating with other artists-researchers to create virtual soundwalks in latent spaces computed by deep learning, as an art-based method to illuminate spatial attributes of the deepscape [START_REF] Scurto | Soundwalking deep latent spaces[END_REF]. We are also exploring computational methods to map bodily movements with soundscape modulations by deep learning to stimulate embodied ways of navigating, and producing knowledge, through/out the deepscape. I am looking forward to further suggestions from practitioners and researchers in other fields.

Conclusion

This paper presented early artistic, conceptual and technical work toward practicing and theorising through/out the deepscape-the planetary media computation intensively led by corporate infrastructures of AI over material, human and cultural resources. I proposed deep listening experiences of deep learning-generated continual flow of soundscape to raise awareness of the deepscape, by recalling the landscapes that are getting exploited by infrastructures of AI. By including non-human voices and planetary scales within research and practice with deep learning, this research-creation thus sought to resist the anthropocentric culture of modern AI research. I am looking forward to further art and science work that might be engaged to reveal and reconfigure the deepscape in depth.

Figure 1 :

 1 Figure 1: Transversal soundscape dataset. In orange: The 28 places where I led online field recording over one week-end in late April 2022. In grey: all microphones previously opened on Locustream since 2006 [49].

Figure 2 :

 2 Figure 2: Transversal soundscape dataset spatiotemporalities.

https://github.com/domkirke/RAVE/tree/multichannel

https://github.com/acids-ircam/nn tilde

http://locus.creacast.com:9001/deepscape transversal.ogg

https://soundcloud.com/hugoscurto/deepscape-transversalcirque-electrique-20221105

Acknowledgments

Authors would like to thank audience members who took part in deep listening experiences, as well as humans and non-humans who contributed to the transversal soundscape dataset, and research-creation collaborators Anne Dubos, Antoine Caillon, Bertha Bermudez, Emanuele Quinz, Jan Schacher, Lucile Vareilles, Philippe Esling, Peter Sinclair, Samuel Bianchini, Vincent Rioux, et al.

Authors Biographies

Hugo Scurto is an artist, designer and researcher, born and based in Marseille. Their research employs art, design and science to craft, prototype and diffract machine learning in an ecology of music. Their practice consists in creating, listening and performing with learning machines that reveal and reshape our musical entanglements with our environments. Hugo is currently postdoc at EUR ArTeC (Paris 8 / EnsadLab), and co-founding member of w.lfg.ng, a post-AI music collective. Before this, they completed a PhD at IRCAM, graduated in Physics from École Normale Supérieure Paris-Saclay, and were visiting research student at Department of Computing, Goldsmiths University of London.

Axel Chemla-Romeu-Santos is a composer, performer and researcher, born and based in Paris. Their scientific research focuses on generative models, bayesian learning, and signal processing to design unsupervised sound synthesis approaches based on perceptual inference, symbolic extraction, and raw signal generation. Their artistic work focuses on the creative aspects of such algorithms in composition and performance configurations. Axel is currently postdoc in the ACIDS group of IRCAM, CNRS, Sorbonne Université, and a co-founding member of w.lfg.ng. They completed a PhD in Deep Learning and Sound Synthesis between IRCAM and the Laboratorio d'Informatica Musicale, and graduated from CRR93 cursus of Computer Music.