Miao Zhao

Rui-Qi Wang
email: ruiqi.wang@nlpr.ia.ac.cn

Fei Yin
email: fyin@nlpr.ia.ac.cn

Xu-Yao Zhang

Lin-Lin Huang
email: huangll@bjtu.edu.cn

Jean-Marc Ogier
email: jean-marc.ogier@univ-lr.fr

Fast Text/non-text Image Classification with Knowledge Distillation

Keywords: High speed, Convolutional neural network, Knowledge distillation

How to efficiently judge whether a natural image contains texts or not is an important problem. Since text detection and recognition algorithms are usually time-consuming, and it is unnecessary to run them on images that do not contain any texts. In this paper, we investigate this problem from two perspectives: the speed and the accuracy. First, to achieve high speed for efficient filtering large number of images especially on CPU, we propose using small and shallow convolutional neural network, where the features from different layers are adaptively pooled into certain sizes to overcome difficulties caused by multiple scales and various locations. Although this can achieve high speed but its accuracy is not satisfactory due to limited capacity of small network. Therefore, our second contribution is using the knowledge distillation to improve the accuracy of the small network, by constructing a larger and deeper neural network as teacher network to instruct the learning process of the small network. With the above two strategies, we can achieve both high speed and high accuracy for filtering scene text images. Experimental results on a benchmark dataset have shown the effectiveness of our method: the teacher network yields state-of-the-art performance, and the distilled small network achieves high performance while maintaining high speed which is 176 times faster on CPU and 3.8 times faster on GPU than a compared benchmark method.

I. INTRODUCTION

Scene texts in natural images convey important semantic information for applications such as autonomous driving, security surveillance, real-time translation, image retrieval human-computer interface, etc. Therefore, the detection and recognition of scene texts have attracted tremendous attention [START_REF] Zhu | Scene text detection and recognition: Recent advances and future trends[END_REF], in recent years and have made great progress. Among the numerous methods proposed so far, deep neural network (DNN) [START_REF] Ma | Arbitrary-oriented scene text detection via rotation proposals[END_REF], [START_REF] He | Text-attentional convolutional neural network for scene text detection[END_REF], based ones have achieved superior performance for both text detection and recognition. DNN based methods, however, are computational expensive and usually need parallel computation hardware such as GPU, which is quite energy consuming. When there are millions of images to process per hour in a typical application scenario such as webpages, the time and energy consumption caused by text detection and recognition on all images is unacceptable. According to [START_REF] Antonacopoulos | Accessing textual information embedded in internet images[END_REF], the majority (76%) of words embedded in images do not appear elsewhere in the main text on WWW pages. In order to obtain this part of text information, text detection and recognition on all images will consume a lot of time. Considering that only 10%-15% of natural images contain texts according to a dataset collected from social networks [START_REF] Bai | Text/nontext image classification in the wild with convolutional neural networks[END_REF], a fast algorithm for quickly filtering images that contain texts for further processing will largely save the computing.

Compared with previous work on document image [START_REF] Delaye | Text/non-text classification in online handwritten documents with conditional random fields[END_REF] and video frame [START_REF] Sharma | Piece-wise linearity based method for text frame classification in video[END_REF], scene text image filtering is a novel and difficult task. Some examples of text and non-text scene images are shown in Fig. 1. The discrimination of text/non-text scene images is not a trivial problem, as the positions of possible texts are unknown, and the problem faces the same challenges as text detection and recognition: cluttered background, variability of text appearance, scale, illumination and perspective. To save gross processing time, a fast filtering algorithm is desired to detect natural images containing texts (briefly called as scene text images) from the pool of images with high recall rate and precision. The detected images occupy a small portion of the pool, such that the gross processing time for deep text detection and recognition can be saved largely.

Previous methods have been proposed for distinguishing text/non-text images based on connected components [START_REF] Le | Text and non-text segmentation based on connected component features[END_REF] and neural networks [START_REF] Bai | Text/nontext image classification in the wild with convolutional neural networks[END_REF], [START_REF] Lyu | Distinguishing text/non-text natural images with multi-dimensional recurrent neural networks[END_REF]. Connected component based methods perform feature extraction and classification on connected components, but the segmentation of connected components is not trivial due to the cluttering of scene images. Methods based on neural networks, particularly DNNs, can learn good features automatically, and to overcome the variability of text scales and locations, the image are usually divided into regions, and the region-level features extracted by CNN are processed sequentially or independently. However, the computational overhead of the DNN based filtering methods are still considerable.

Although the existing methods have proved that regionlevel image classification can improve the accuracy of scene text image recognition, it is required to set appropriate criteria carefully for region-level labeling of images. It is a difficult problem how to set such an appropriate criterion.

When the judgment condition is too strict, some text regions are judged as non-text regions, and when the judgment condition is too loose, some image areas that hardly overlap with the text areas are determined as text areas. Therefore, the region-level labels contain a lot of noise. Especially when the network capacity is insufficient, the noise in the regionlevel labels limit the improvement of classification performance greatly. Therefore, it is difficult for existing methods to discriminate the text and non-text images quickly while ensuring high performance. Moreover, the existing methods are all based on GPU. When these methods run on the CPU, their speed is unsatisfactory. Compared with CPU, GPU is more expensive and requires more energy consumption. Therefore, it is necessary to design an algorithm that can quickly recognize the scene text image on the CPU.

Our proposed algorithm focuses on fast and efficient scene text image filtering. In order to achieve fast processing speed, we design a small convolutional neural network, which classifies the whole image by considering sub-regions of different scales. Nevertheless, due to the impact of network capacity and label noise, it is difficult for the small network to achieve high performance. To tackle this problem, we design a large network which can obtain high classification accuracy. We use the large network as teacher network and the small network as student network. Our method maximizes the transfer of knowledge from the teacher network to the student network through combination of knowledge distillation methods, so that student network can achieve high speed and high performance at the same time. We conduct two-stage training on the student network. In the first stage, the student network is well initialized by activating boundary knowledge distillation. In the second stage, we solve the problem of region-level label containing noise by means of soft label knowledge distillation which fits region-level prediction of teacher network, and improve the performance of student network by means of adversarial knowledge distillation which fits region-level features of teacher network.

The rest of this paper is organized as follwed. Section II reviews related work. In Section III, we show the details of our method. In section IV, we validate our method by experiments on the benchmark dataset. Section V concludes our work.

II. RELATED WORK

In this section, we introduce some methods of scene text image filtering and knowledge distillation. Moreover, the motivation of our algorithm is also discussed.

A. Scene Text Image Filtering

To the best of our knowledge, there are few researches on scene text image filtering. CNN coding [START_REF] Zhang | Automatic discrimination of text and non-text natural images[END_REF] adopts the method of combining Maximally Stable Extremal Regions (MSER), CNN and Bag-of-Words (BoW) to conduct scene text image filtering. CMDRNN [START_REF] Lyu | Distinguishing text/non-text natural images with multi-dimensional recurrent neural networks[END_REF] extracts the image feature through CNN, and then obtains the region-level features which contain context information by RNN. In the end, this method determines whether the whole image contains text by judging whether the local region of the image contains text or not. MSP-Net [START_REF] Bai | Text/nontext image classification in the wild with convolutional neural networks[END_REF] divides the image into image subregions of different scales, and predicts if these image subregions contain text by CNN, so as to predict whether the whole image contain text or not.

MSP-Net can achieve state-of-the-art performance in the scene text image filtering task. However, MSP-Net requires a huge network to achieve the scene text image filtering. When the device resource is limited, MSP-Net can not meet the need of real-time processing. Another disadvantage of MSP-Net is that the noise of region-level labels hinders the further improvement of network performance. Inspired by MSP-Net, we also adopt the method of obtaining imagelevel prediction through multi scale region-level predictions. The main difference compared with MSP-Net is that we use a very lightweight network, which can achieve fast scene text image filtering in both CPU and GPU. Through knowledge distillation, it can obtain relatively high performance.

B. Knowledge Distillation

Knowledge distillation is an important model acceleration method, which can make the network performance improvement by knowledge transfer. Hinton et al. [START_REF] Hinton | Distilling the knowledge in a neural network[END_REF] first put forward the concept of knowledge distillation and believed that knowledge transfer could be realized by fitting the soft label generated by the teacher network. Romero et al. [START_REF] Romero | Fitnets: Hints for thin deep nets[END_REF] extends the knowledge distillation to the feature map of intermediate layers in DNN. Zagoruyko et al. [START_REF] Zagoruyko | Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer[END_REF] proposed that knowledge transfer between networks can be accomplished by means of attention map. Byeongho Heo et al. [START_REF] Heo | Knowledge transfer via distillation of activation boundaries formed by hidden neurons[END_REF] proposed to transfer the activation boundary formed by hidden neurons in the pre-training stage to obtain efficient knowledge distillation. Peiye Liu et al. [START_REF] Liu | Ktan: Knowledge transfer adversarial network[END_REF] and Wei-Chun Chen et al. [START_REF] Chen | Knowledge distillation with feature maps for image classification[END_REF] enabled the student network to imitate the intermediate representations of the teacher network by means of adversarial strategies. In this section, we will present the details of our proposed method.

A. Teacher Network

Inspired by MSP-Net [START_REF] Bai | Text/nontext image classification in the wild with convolutional neural networks[END_REF], we design the teacher network as shown in the Fig. 2. In the teacher network, we use the feature extraction network of VGG16 [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] as the image feature extractor. The teacher network does not require the size of the input image to be fixed, so the features extracted from the input image of uncertain shape C * W * H should have uncertain shape C ′ * W ′ * H ′ . In order to obtain region-level features, image features from the last two layers are pooled to fixed sizes (5*5, 3*3). To obtain richer information, we add feature maps of the same size together to obtain multi-scale feature maps.

The features on the feature map correspond to different image sub-regions. In order to instruct the student network, region-level feature is linear mapped in batch into a new space. These mapped regional-level features will be used for knowledge distillation. The last layer of the network is used to output the prediction of each sub-region of the image. Image-level prediction is obtained by logic OR on the region-level prediction.

B. Student Network

Student network is more straightforward than teacher network and its architecture is shown in Fig. 3. It contains 6 convolution layers as the image feature extractor. To improve the speed of the student network, we do not fuse multiple layers of features. It obtains multi scale features directly from the last two convolution layers after regularization. The student network obtains the feature maps with the size of 5*5 or 3*3 after adaptive pooling. Then we use the two-layer full-connected network to process the regionlevel features. The first layer maps the region-level features from the original space to the feature space shared with the teacher network. These region-level features will be used for knowledge distillation. Afterwards, the features are used for classifying the different regions in the origin image, and the prediction of whole image is the logical OR of predictions of all regions.

C. Adversarial Knowledge Distillation Network

In the second stage of student network training, the adversarial knowledge distillation network is shown in Fig. 4. Adversarial knowledge distillation network consists of three parts: teacher network, student network and discriminant network. The teacher network and the student network simultaneously output the predictions of each image region and the region-level features mapped to the same space. In the part of soft label knowledge distillation, we take the regional-level prediction of teacher network as the target, and then calculate the cross entropy loss. In the part of adversarial knowledge distillation, we draw lessons from the thought of GAN [START_REF] Goodfellow | Generative adversarial nets[END_REF]. We regard the region-level feature distribution of teacher network as the real distribution, and the region-level feature distribution of student network as the fake distribution. In the process of training, the discriminant network learns to judge whether the input region-level features come from the teacher network or not, and student network learns to cheat the discriminant network during the training process. With the adversarial knowledge distillation, the student network will imitate the teacher network to produce region-level features. BEGAN [START_REF] Berthelot | Began: boundary equilibrium generative adversarial networks[END_REF] can be easily trained in many variations of GAN [START_REF] Radford | Unsupervised representation learning with deep convolutional generative adversarial networks[END_REF], [START_REF] Berthelot | Began: boundary equilibrium generative adversarial networks[END_REF]. Encouraged by BEGAN, we use the auto-encoder as the discriminant network. In the form of the combination of adversarial knowledge distillation and soft label knowledge distillation, the student network learns from the region-level soft labels and region-level features generated by the teacher network.

D. Network Training

In the training process of teacher network, we need to divide the image into sub-regions of different scales(3*3, 5*5), and then determine the labels of different sub-regions through a judgment condition. In MSP-Net [START_REF] Bai | Text/nontext image classification in the wild with convolutional neural networks[END_REF], only when the proportion of the text image area exceeds 0.05 and the height of the text line exceeds 1/2 of the height of the image area, the corresponding label is set to 1. However, text has the feature of scale diversity, and some text image areas can not meet the condition. Finally, as long as the overlapping ratio between the text area and the image area reaches 0.01, we set the label of the image area as 1. The following experiments prove that this setting can significantly improve the performance of scene text image filtering.

The training of student network is divided into two stages: pre-training and formal training.

In the pre-training stage, we use activation boundary knowledge distillation to train the student network. ReLU activation divided the input feature space into two parts, and Byeongho Heo et al. [START_REF] Heo | Knowledge transfer via distillation of activation boundaries formed by hidden neurons[END_REF] regarded the hyperplane between the two parts as the activation boundary. By fitting the activation boundary, the student network can learn a lot of knowledge from the teacher network. In our work, we hope that the student network can transfer the activation boundary from the teacher network in the feature space of image subregion. The activation boundary loss here is calculated as follows:

L A = 1 M M i=1 d T i ⊙ r u -S i + 1 -d T i ⊙ r u + S i 2 2 ( 1
)
where d is indicator function, it outputs 0 when its input value is greater than 0, or it outputs 1. r stands for ReLU. u is a margin value. ⊙ represents element-wise product of vectors. These symbols described below are consistent in later expressions. T i is the mapped region-level feature extracted by teacher network. S i is the mapped region-level feature extracted by student network. The subscription i represents the i-th sub-regions of the input image. M is the number of subregions obtained by image segmentation(34 in this paper).

Through further analysis, we can find that activation boundary transfer enables the response value of students' neurons to approach a positive critical value u when the teacher network's neurons are activated, while the corresponding value of student network's neurons approaches -u when the teacher network's neurons are deactivated. In this way, the student network imitates the activation boundary of the regional-level features of the teacher network and maximizes the activation boundary interval.

In the formal training phase, we optimize the student network which has be well initialized. In this stage we use both

L Stu = L K + bL G (2)
L K represents the soft label knowledge distillation loss function. L G represents the adversarial knowledge distillation loss function. b (0.2 in this paper) is a loss balance parameter. With the minimization of the loss function, the student network can generate region-level representation similar to the teacher network and accurately predict the image region.

The loss function of soft label knowledge distillation is defined as follows:

L k = - 1 M M i=1 l i log p i + 1 -l i log 1 -p i (3)
l i denotes the probability of that certain region, which is predicted to be positive by the teacher network. p i denotes the probability that certain region is predicted to be positive by the student network.

In the adversarial knowledge distillation, the discriminant network is expected to judge whether the input features come from the teacher network or from the student network, and the student network is expected to be able to deceive the discriminant network. When the training reaches equilibrium, The regional-level feature distribution generated by the student network can approximately fit the regionallevel features generated by the teacher network. According to BEGAN [START_REF] Berthelot | Began: boundary equilibrium generative adversarial networks[END_REF], we construct the adversarial knowledge distillation loss function as follows:

L D = 1 M M i=1 D T i -T i - k t M M i=1 D S i -S i (4)
L G = 1 M M i=1 D S i -S i (5)
L D is the loss function of the discriminant network. L G is the adversarial knowledge distillation loss function of the student network. D is the discriminant network. k t is a control parameter, and it can control how much emphasis is put on L G . k t (the initial value is 0) will change with the training process, and its expression is calculated as follows:

k t+1 =k t + f k * n M M i=1 D T i -T i - 1 M M i=1 D S i -S i (6)
f k (1e-3 in this paper) controls the change rate of k t . n (0.75 in this paper) is a relaxation variable. n controls how much the student network needs to fit the regionallevel feature distribution of the teacher network when the training reaches equilibrium.

IV. EXPERIMENTS

In this section, we perform several experiments to validate the effectiveness and efficiency of our method on a challenging public benchmark for text/non-text natural image discrimination.

A. Dataset

We use the dataset named TextDis released by Zhang et al. [START_REF] Zhang | Automatic discrimination of text and non-text natural images[END_REF], which focuses on text image discrimination. In this dataset, the train set contains 5302 text images and 6000 non-text images, the test set contains 2000 text images and 2000 non-text images. Each text image has a ground truth file which contains the bounding box information of text it contains. We split the images into 3 * 3 and 5 * 5 regions for experiments. For text images, with bounding boxes, we can obtain the overlap area of certain image region with all text regions. A threshold (1% particularly in this paper) is set to label image regions positive according to the ratio of overlap area over image region. Apparently regions from non-text images are all labeled negative. Although both teacher network and student network have no requirements for input size, we rescale the input to make the short edge 256 pixel. The input is normalized before feed into the network. Data is augmented with flip and rotation.

B. Training Details

We use Stochastic Gradient Descent (SGD) with minibatch size of 1 to optimize the training process. For teacher network, learning rate is 1e-4. For student network, the whole training process consists of two stages. In the pretraining stage, the learning rate is 1e-5. In the formal training stage, the initial learning rate of the student network is 1e-4, and when the train loss stop improving, the learning rate is reduced to 1/10. The learning rate of the discriminator network is always twice that of the student network.

C. Experiment Results

We test our method on TextDis dataset and compare it with MSP-Net [START_REF] Bai | Text/nontext image classification in the wild with convolutional neural networks[END_REF] on precision, recall, F-measure ,time cost and parameters. Time cost means only time cost during model computing otherwise data IO consumes most time and makes the difference insignificant. CPU time cost experiments are performed on a platform with one Intel(R) Xeon(R) CUP E5-2620 0 @2.00GHz and 256G RAM. GUP time cost experiments are performed on a single Nvidia(R) GTX TITAN with 12G VRAM.

The experiments result on TextDis dataset is showed in TABLE I. As shown in the Fig. 2, our network can further give the prediction results of each region, which is conducive to subsequent text detection and recognition. The recall, precision and F-measure of MSP-Net is from [START_REF] Bai | Text/nontext image classification in the wild with convolutional neural networks[END_REF]. We reimplement MSP-Net and test the runing time on GPU and CPU. The results of CNN Coding and CMDRNN are from [START_REF] Zhang | Automatic discrimination of text and non-text natural images[END_REF], [START_REF] Lyu | Distinguishing text/non-text natural images with multi-dimensional recurrent neural networks[END_REF].All models are developed with Pytorch 4.0.1. Since we use the same experimental dataset, the experimental results are comparable. Compared with MSP-Net [START_REF] Bai | Text/nontext image classification in the wild with convolutional neural networks[END_REF], our teacher network achieves higher speed under higher performance. This proves that the more simplified network structure can achieve higher performance under appropriate text region discrimination conditions, and further proves that the regional-level annotation will greatly affect the network performance.

To demonstrate the validity of our knowledge distillation method, we conduct further comparative experiments. The experiments result is showed in TABLE II. The region-level labels obtained by judging conditions are called hard label. Compared with the region-level labeling by discriminating conditions, soft label knowledge distillation significantly improves the performance of student network. The experiment proves that soft label knowledge distillation can solve the problem of region-level labels containing noise. Furthermore, the experimental results show that the adversarial knowledge distillation can improve the performance of student network. Finally, the student network trained through the full training process achieves the best performance. It shows that the pre-training makes the student network get a good initialization.

V. C ONCLUSION

In this paper, we propose a method for fast text/nontext natural images classification. It can handle the difficulties caused by various locations and multi-scale. We enable student network to be adequately pre-trained by activating boundary knowledge distillation. Through the soft label knowledge distillation and the adversarial knowledge distillation, our method obtains the high performance. Experiments on public dataset shows our method achieves super fast speed than any other methods and keeps high accuracy. In the future, we would like to integrate our method as a pretreatment module into text analysis system.

Figure 1 .

 1 Figure 1. Examples for text image and non-text image.

Figure 2 .

 2 Figure 2. Architechture of teacher network

Figure 3 .

 3 Figure 3. Architechture of student network

Figure 4 .Figure 5 .

 45 Figure 4. Architechture of adversarial knowledge distillation network

Table I RESULTS

 I COMPARISION OF DIFFERENT METHOD.I T SHOULD BE NOTICED THAT MSP-NET IS TRAINED WITH LARGER INPUT IMAGE HIGHT 500 PIXEL.*INDICATES THAT THE RELEVANT DATA IS ENTIRELY FROM THE ORIGINAL PAPER.

	Method		Recall	Precision	F-measure	Time Cost (CPU) Time Cost (GPU)	Params
	CNN Coding*	89.8%	90.3%	90.1%	-	460ms	-
	CMDRNN*	90.4%	93.3%	91.8%	-	90ms	-
	MSP-Net	95.4%	93.7%	94.6%	9.53s	5.38ms	136.40M
	Teacher Network	97.5%	94.0%	95.7%	2.24s	2.60ms	16.03M
	Student Network	94.0%	92.0%	93.0%	54.03ms	1.40ms	0.087M
	Table II
	EFFECT OF KONWLEGE DISTILLATION
	Method	Recall	Precision	F-measure
	Hard label	91.0%	88.0%		89.5%
	Soft label	92.9%	90.2%		91.5%
	Soft label + Adversarial	94.3%	90.7%		92.5%
	Our proposed	94.0%	92.0%		93.0%

VI. ACKNOWLEDGEMENT

This work is supported by Natural Science Foundation of China under Grant No. 61733007, 61573355, 61721004.


Fatima-Ezzahra Ennaji

Joël Fagot
email: joel.fagot@univ-amu.fr

Pascal Belin

Categorization of vocal and non-vocal stimuli in Guinea baboons (Papio papio)

Keywords: categorization, voice perception, conspecific vocalizations, non-human primate, behavior

Categorization of vocal sounds apart from other sounds is one of the key abilities in human voice processing, but whether this ability is present in other animals, particularly non-human primates, remains unclear. In the present study, 25 socially-housed Guinea baboons (Papio papio) were tested on a vocal/non-vocal categorization task using Go/Nogo paradigm implemented on freely accessible automated learning devices. Three individuals from the group successfully learned to sort Grunt vocalizations from non-vocal sounds, and they generalized to new stimuli from the two categories, indicating that some baboons have the ability to develop open-ended categories in the auditory domain. Contrary to our hypothesis based on the human literature, these monkeys learned the non-vocal category faster than the Grunt category. Moreover, they failed to generalize their classification to new classes of conspecific vocalizations (Wahoo, bark, Yak, and copulation calls), and they categorized human vocalizations in the non-vocal category, suggesting that they had failed to represent the task as a vocal vs non vocal categorization problem. Thus, our results do not confirm the existence of a separate perceptual category for conspecific vocalizations in baboons. Interestingly, the three successful baboons are the youngest of the group, with less training in visual tasks, which supports previous reports of age and learning history as crucial factors in auditory laboratory experiments.

Introduction

Vocal perception is a key component of primate communication. Because primates use vocalizations in their social environment and during social interactions, it is crucial that they accurately analyze and process the available vocal information. As proposed by a neurocognitive model of human voice perception [START_REF] Belin | Thinking the voice: Neural correlates of voice perception[END_REF], a basic building block of vocal perception is the ability to differentiate conspecific vocalizations (CVs) from other stimuli, i.e., the recognition that a given sound is emitted by the vocal tract of another individual of the same species. Additional neural processes then interact to extract different types of information contained in CVs, such as age, sex, identity, and affective state.

Preexisting cognitive abilities and neural mechanisms involved in differentiating vocal and non-vocal sounds as well as extracting relevant information in CVs have likely emerged in all primates, as we can extract various information even when speech is not available (e.g., identity: [START_REF] Latinus | Human voice perception[END_REF]gender: Mullennix et al., 1995), like non-human primates do while processing CVs. Thus, a crucial question arises about voice processing, does it involve specialized mechanisms not used for other non-vocal sounds from the environment and are these mechanisms similar in both human and non-human primates?

Several neuroimaging studies revealed evidence for cortical mechanisms were activated by vocal stimuli more than by other non-vocal stimuli. In humans, neural correlates of voice perception were found along the superior bank of the Superior Temporal Sulcus (STS) located in the auditory cortex [START_REF] Belin | Voice-selective areas in human auditory cortex[END_REF][START_REF] Belin | Human temporal-lobe response to vocal sounds[END_REF]. Other cortical areas were also reported to be highly sensitive to vocalizations in non-human primates such as in macaque monkeys in the anterior temporal lobe [START_REF] Bodin | Functionally homologous representation of vocalizations in the auditory cortex of humans and macaques[END_REF][START_REF] Petkov | A voice region in the monkey brain[END_REF]. These findings suggest the existence of voice selective areas actively involved in processing and extracting information carried by CVs. This neural sensitivity to CVs is revealed in humans by a behavioral advantage for categorizing or detecting voice stimuli, in comparison to other non-vocal sounds. For instance, [START_REF] Agus | Characteristics of human voice processing[END_REF] found that the processing of vocal stimuli was faster and more accurate in a Go/Nogo recognition task, compared to a range of musical sounds. Likewise, vocal stimuli are detected fastest (as fast as 4 ms) compared to instrument sounds [START_REF] Agus | Characteristics of human voice processing[END_REF][START_REF] Suied | Auditory gist[END_REF]. In one electrophysiological study, [START_REF] Levy | Processing specificity for human voice stimuli: Electrophysiological evidence[END_REF] reported a voice-specific response linked with greater processing specificity for human voices compared with musical instruments at around 320ms after onset.

The above studies, and others, altogether suggest that voice stimuli are processed differently from other acoustic stimuli [START_REF] Charest | Electrophysiological evidence for an early processing of human voices[END_REF][START_REF] Levy | Processing specificity for human voice stimuli: Electrophysiological evidence[END_REF].

To further investigate the behavioral mechanisms of voice processing and their evolution, we asked whether non-human primates would show evidence of a separate perceptual representation for CVs, as indicated by their ability to be trained to categorize CVs apart from non-vocal sounds. To our knowledge, there is no behavioral evidence that monkeys possess this ability. This study attempts to fill this gap and investigate a core function of voice processing: categorization of CVs and non-vocal sounds.

Here, we chose the Guinea baboon (Papio papio) as a study model. Our choice of the Guinea baboons is supported by the recent demonstration that their vocal productions system shares anatomical (e.g., tongue anatomy) and functional traits (e.g., production of vowel-like sound; [START_REF] Boë | Evidence of a Vocalic Proto-System in the Baboon (Papio papio) Suggests Pre-Hominin Speech Precursors[END_REF] with that of humans, and that these animals also exhibit some of the key cognitive components of vocal processing. Indeed, several playback studies demonstrated their capacity to extract valuable information in their CVs. [START_REF] Engh | Who, me? Can baboons infer the target of vocalizations[END_REF] for instance reported that female baboons responded more to the playback of a dominant female's threat-grunt in case of recent threatening than when the same female had recently groomed them, suggesting that these baboons have interpreted the threat-grunts based on prior social interactions, and more importantly took into account the signaler's identity when processing these vocalizations [START_REF] Engh | Who, me? Can baboons infer the target of vocalizations[END_REF]. Similarly, the response to the playback of reconciliatory grunts is stronger in baboons when the receiver had a recent agonistic interaction with the emitter, in comparison to controls, suggesting that they are capable of distinguishing different identities based on their grunts and remembering their recent aggressive interactions [START_REF] Owren | The acoustic features of vowel-like grunt calls in chacma baboons ( Papio cyncephalus ursinus ): Implications for production processes and functions[END_REF]. Together, these findings make the Guinea baboon an interesting model for the study of voice perception emergence, and a perfect subject in a study on vocal and non-vocal categorization.

In the present study, we trained 25 Guinea baboons to discriminate vocal from non-vocal sounds in a Go/Nogo task. First, we presented a set of baboon's vocalizations (Grunts) and non-vocal auditory stimuli. We then assessed post-training performance in generalization trials using novel exemplars of the two categories. Second, we presented a larger vocal category using four new classes of baboon's vocalizations previously described in this species [START_REF] Kemp | Vocal repertoire of captive Guinea baboons (Papio papio)[END_REF]: barks, yaks, wahoo, and copulation calls and new exemplars of nonvocal auditory stimuli. We predicted that the baboons would succeed in the first categorization task and would generalize to novel auditory stimuli. Considering the ecological relevance of the grunts, we expected that the baboons would express faster learning and greater categorization performance for the vocal than for the non-vocal category. We also predicted that the baboons would accurately categorize the four new classes of CVs in the vocal category, similar to the grunt vocalization. We further inspected their performance in case of human voices from both sexes at different ages (e.g., female laughter, male adult cry, infant cry). Finally, we examined baboon's performance when white noise is added to the vocal and non-vocal stimuli. This second experiment aimed to establish psychometric functions for both categories, depending on the amount of noise added to the vocal and nonvocal stimuli. With a similar procedure, [START_REF] Heimbauer | A chimpanzee recognizes varied acoustical versions of sine-wave and noise-vocoded speech[END_REF] reported that a female language-trained chimpanzee had similar performance pattern to that of human participants in a speech identification task, using noise-vocoded words (i.e., these stimuli were made from noise bands modeled on the frequency and amplitude of natural speech).

They suggested that this chimpanzee might have used comparable strategies to identify speech words and relied on the same cues as humans [START_REF] Heimbauer | A chimpanzee recognizes varied acoustical versions of sine-wave and noise-vocoded speech[END_REF] . If voice stimuli have a special status in our study on baboons, then we expect that baboons will perform better in noise for the vocal than for the non-vocal category.

Methods and results

Experiment 1

Experiment 1 aimed to test the ability of baboons to categorize CVs apart from non-vocal sounds using the Go/Nogo task. It further investigated how baboons generalized their performance when novel stimuli were introduced in the task. The first part of the experiment focused on one type of CVs: the grunt. The second part explored a larger set of CVs from the baboons' vocal repertoire. And the third part examined categorization performance in case of human voices.

Material and methods

Subjects

The subjects were 25 Guinea baboons (Papio papio) between 3 and 24 years old. They were housed in two social groups in the CNRS (Centre national de la recherche scientifique) primate center CNRS, UAR846, Rousset, France. The first group comprised two adult males and three adult females and the second of six males and thirteen females. Both groups were housed in an outdoor enclosure connected to indoor housing used primarily at night and during feeding hours. Baboons were fed daily at 5pm (fruits, vegetables, and monkey chows), and water was provided ad libitum within each enclosure. Subjects were a year older during the second and third parts of experiment 1.

Ethics statement

This research was carried out in accordance with European Union and French ethical standards and received approval from the French Ministère de l'Education Nationale et de la Recherche (approval no. APAFIS-2717-2015111708173794-V3).

Apparatus

The study was conducted using a total of 14 operant conditioning test systems, called automated learning devices for monkeys (ALDM) which have been described in detail in [START_REF] Fagot | Automatic testing of cognitive performance in baboons maintained in social groups[END_REF]. Each test system comprised a touch screen for stimulus presentation, a food dispenser for reward delivery, two speakers for auditory stimulus delivery, and a RFID equipment for an automatic identification of the animals once they enter a test system. The larger group of baboons had free access to 10 ALDM test systems installed within two trailers connected to the enclosure where the baboons live. The smaller group had access to 4 ALDM test systems, installed within another trailer connected to the baboons' enclosure.

Each time a baboon enters an ALDM test system, they are automatically identified by the RFID microchips implanted in its arms. The computer then gets information on that subject from a server device controlling all the ALDM test systems, to resume the task where the subject left its previous trial even if on another system. This procedure allows free-self testing on a 24h schedule, with no need to capture the subject for testing.

General procedure

The auditory task was based on a Go/Nogo response rule, with vocal sounds (Go) requiring a response, and non-vocal sounds (Nogo) requiring no response; both Go, and Nogo correct responses were rewarded. The Go response consisted of touching the screen where a black square was presented at its center as press button. When a non-vocal stimulus was played, baboons had to avoid touching the black square on the screen to be rewarded (Nogo response).

Automatic identification of the subject triggered the trial. At the beginning of the trial, the computer played a Go (vocal) or Nogo (non-vocal) auditory stimulus and simultaneously displayed a black square which remained on the screen during a maximum of two seconds.

During that delay, the subject had to touch the square in case of Go trials or had to refrain touching it otherwise. All correct (Go or Nogo) responses were rewarded by a few grains of dry wheat within the ALDM system. Incorrect Go or Nogo responses (e.g., touching the screen in Nogo trial or not touching it in Go trials) were followed by a 3 second time-out indicated by a green screen. A minimum inter-trial interval of 3 seconds was applied between two consecutive trials, but that delay could be much longer as the subject could stop and resume testing at will. The general timeline of the Go/Nogo task is illustrated in Figure 1.

Auditory stimuli

Auditory stimuli were from two categories: a vocal and non-vocal category. The vocal one consisted of baboon vocalizations recorded previously from the same social group. The vocalizations chosen from their vocal repertoire in Experiment 1.1 are the "grunts," as they are the most common affiliative vocalizations, also known to be used by all age-sex groups, usually to elicit interactions [START_REF] Kemp | Vocal repertoire of captive Guinea baboons (Papio papio)[END_REF]. The non-vocal category consisted in various natural (e.g., water ripple, flowing stream) and environmental sounds (e.g., police noise, bells, etc.). There were 95 stimuli in total, 50 stimuli for the non-vocal category and 45 stimuli for the vocal one (due to smaller set of grunt recordings). Stimuli (22050Hz, mono) were manually cut to a duration of 500ms and played at 65dB. White noise at an amplitude of 20% was added to all stimuli to mask differences in intrinsic noise level.

Training and test procedure

Experiment 1.1 involved six phases which will be referred to below as P1 to P6. They are described in Table 1. The rationale of our procedure was to introduce novel stimuli in each phase, to assess if, and when in training, an immediate transfer of performance occurred at the very first presentation of the novel stimuli. If the subjects failed to demonstrate above chance performance at the first presentation of the novel stimuli, then they were continuously exposed to these stimuli and previously used stimuli, until they reached learning criterion, at which stage the next phase was introduced for an additional test of generalization. This procedure continued from phase 1 to 6, at which point we stopped the experiment.

In all phases, trials were organized in blocks of 80 trials (P1, P2 and P3), 60 trials (P5) and 100 trials (P4 and P6) with each block containing an equal number of Go and Nogo trials.

Within each phase, trial blocks were repeated in a randomized order until the subject achieved 80% correct or more for both categories. Also, a correction procedure was implemented to assist baboons during this training: in case of error, the same trial was repeated up to 3 times consecutively to give the baboon a chance to respond correctly to the stimulus, before presenting the next trial of the block.

Phase P1 consisted of presenting one single vocalization (grunt) and one non-vocal sound.

Phase P2 followed the same procedure as P1 but with three vocalizations (grunts) and three non-vocal sounds, including those already used in P1 (i.e., 2 additional stimuli per category).

As described in Table 1, a progressive expansion of the stimulus set size followed in the next four phases (P3, P4, P5 and P6) to reach a total of 50 stimuli in the non-vocal category and 45 stimuli in the vocal one.

Number of Stimuli

Vocal

Non-vocal

Phases Baseline Novel Baseline Novel P1 1 - 1 - P2 1 2 1 2 P3 3 2 3 2 P4 5

Results

We recorded a total of 300958 trials for the 25 baboons (corrected trials from the correction procedure not included). Twenty baboons failed to learn the task and never reached the learning criterion in P1, their performance is reported in supplemental materials available at http://doi.org/10.17605/OSF.IO/DZEF4/. Two baboons (Mako and Lomé) reached criterion in P2 and P3 respectively, and three baboons (Muse, Mali and Nekke) finished the entire experiment and reached the criterion in all phases (P1 to P6). Table 2 summarizes the learning performance of these five individuals. with trial phase (P1, P2, P3, P4, P5, P6) as factor using the number of blocks required to reach 247 criterion as an independent variable. For Nekke, there was a significant difference in block repetitions between phases, F (5,197) = 30.55, p <0.001. Post hoc comparisons using the Tukey test revealed that the number of blocks required to reach criterion was greater in P3 than in P1 (p = 0.03) and all remaining phases (all ps < 0.001). There were by contrast no reliable difference between P1, P2, P3, P4 and P6. For Muse, there was also a significant difference in block repetitions between phases, F (5,99) = 5.13, p <0.001. Post hoc comparisons using the Tukey test revealed that more blocks were needed in P6 compared to P1 (p <0.001), but we found no reliable difference between P1 and P5 (p = 0.08), P1 and P4

(p = 0.1) and P2 and P6 (p = 0.08). Finally, for Mali, there was a significant difference in block repetitions between phases, F (5,366) =61.72, p <0.001. Post hoc comparisons using the Tukey test revealed that the number of block repetition was greater in P4 than in the other 5 phases (all ps <0.001), and there was no reliable difference between the other phases.

Generalization of categorization

During experiment 1.1, stimuli from both vocal and non-vocal categories were added progressively at each new phase of the experiment. To test whether subject readily generalized the categories they had formed during the previous phases to novel stimuli, we examined scores obtained at the first presentation of each novel stimulus and compared those scores to chance level (see Table 3).

Learning speed difference between the vocal and non-vocal categories

The data presented in Table 3 suggest that the non-vocal category was learned earlier than the vocal category: the generalization of the non-vocal category (Nogo) occurred as soon as P5

for Nekke and Mali. On the other hand, generalization occurred only at P6 for Muse and Mali in the vocal category (Go).

To explore the possibility that the subjects learned one category before the other one, we compared independently for the three individuals the number of block repetitions required to achieve 80% correct in the vocal and non-vocal categories (see Table 4). For each category, it was considered that the subjects reach the learning criterion if (1) it was above 80% correct in the considered category and (2) it was not significantly below chance, as inferred by a twotailed binomial test, in the alternative category. This second criterion was introduced to avoid a consideration of high scores in one category that may reflect purely motor (Go or Nogo)

biases, considering that such biases must be associated to an above chance performance in one category (e.g., 100% in the Go trials in case of a Go response bias) and a below chance performance in the alternative category (i.e., 0% correct in that case). in the considered category. Bold character indicates when a reliable difference was obtained between the two categories, as inferred by a binomial two-tailed test (p<.05). In that case, bold characters refer to the most difficult category to learn.

Results show that the vocal category (Go) required more block repetitions than the non-vocal category (Nogo) in three phases (P2, P3 and P4) for Nekke and in two phases (P1 and P3) for Mali. For Muse, and for all the phases, the number of blocks required to reach criterion in the vocal and non-vocal categories were not statistically different suggesting that these categories were acquired at the same speed. However, for the two baboons Mali and Nekke, more blocks were required when considering the totality of the six phases, suggesting that they have learned the non-vocal category at a faster pace than the vocal one.

Sensitivity and bias

To measure each of the three individuals 's sensitivity and examine any potential bias in their responses, we calculated d' (sensitivity) based on the signal detection theory [START_REF] Macmillan | Response Bias: Characteristics of Detection Theory, Threshold Theory, and "Nonparametric" Indexes[END_REF]. Considering all trials from P6 for each subject, the "hit", "miss", "false alarm", and "correct rejection" rates are determined for the vocal (Go) and non-vocal trials (Nogo) and reported in Table 4. "Hit" corresponds to a correct Go response when a vocal category was played, "miss" is when the subject gave a Nogo response to a vocal stimulus, "false alarm" corresponds to the case where the baboon gives a Go response to a non-vocal category and finally, "correct rejection" is obtained from the correct Nogo response to nonvocal stimuli. d' is then determined for each individual, and for each category: d' = z(H)z(F), where z(H) and z(F) are the z -transforms of "hit" and "false alarm" rates for vocal category, and d'= z(CR) -z(M) where z(CR) and z(M) are the z -transforms of "correct rejection" and "miss" rates for the non-vocal category.

Results indicate that there was no bias in the three baboon's responses, as "hit" rates were higher than "false alarm" rates, and "correction rejection" rates were higher than "miss" rates.

Also, d' values were superior to 0 for all three subjects.

Table 5. Sensitivity and response biases. This table reports the "hit", "miss", "false alarm"

and "correct rejection" rates for each of the three individuals, as well as the d'.

Interim discussion 1

The results presented above suggest that this auditory task was extremely difficult to learn for the baboons. Regardless of the high number of trials achieved by most of the baboons, only 3 successful animals from a pool of 25 were able to meet the accuracy criterion and finish all six phases of the experiment.

As predicted, the three successful subjects could demonstrate an open-ended form of categorical procedure, allowing to correctly categorize novel stimuli at their first presentation.

Further inspection of the learning performance suggests that contrary to our predictions, the non-vocal category was learned faster and better than the vocal category, suggesting a non- vocal advantage in the categorization task. These results will be discussed in greater details below.

Experiment 1.2

After approximately 18 months, we reran Experiment 1.1 as an initial step. The same procedure, apparatus and paradigm were used, with one minor modification in the paradigm:

we added a fixation cross on the screen where subjects had to touch to initiate the trials, and new reward delivery systems (with less noise) were installed. Generalization to novel classes of baboon's vocalizations (Barks, Yaks, Wahoo, and Copulation calls) and novel exemplars of non-vocal stimuli (electronic, music, natural sounds etc.) was tested after retraining. We predicted that the baboons would generalize to novel classes and exemplars from both categories.

Auditory stimuli

In the vocal category, we presented four classes of baboon's vocalization. The stimuli were previously recorded from the same groups as the grunt recordings of Experiment 1.1. The four vocalizations chosen from their repertoire include the barks, yaks, wahoos and copulation calls. Barks are vocalizations produced by both males and females at different age categories.

They were recorded in two contexts: prior to feeding and in the presence of sheep in the surroundings. The yaks are mostly produced by infants, often coupled with a scream in case of a threat or distress. The wahoos are vocalizations typically produced by adult males in response to other wahoos of other individuals. Finally, copulation calls are produced by females (adults, sub-adults and even juvenile) towards the end of copulation [START_REF] Kemp | Vocal repertoire of captive Guinea baboons (Papio papio)[END_REF]. In the non-vocal category, we presented a set of new various musical, environmental, natural, and electronic sounds. Stimuli from both categories (22050 Hz, mono) were manually cut to 500ms duration, then white noise at an amplitude of 20% was added to all stimuli to mask differences in intrinsic noise level. Stimuli were played at a sound pressure level of approximately 75dB(C).

Procedure

The individuals who were successfully retrained in the six phases (P1 to P6) of Experiment 1.1 were tested in Experiment 1.2. Here we used the same Go/Nogo task and general procedure as described above. However, the baboons were presented with one single test and "correct rejection" rates for the novel stimuli for each of the three individuals, as well as the d'.

Generalization of the categorization

Sensitivity and bias

Interim discussion 2

The result show that only the three individuals that succeeded in Experiment 1.1 were able to perform again the categorization task, confirming the reliability of the findings. The results of the generalization tests indicate that the barks and yaks were categorized as non-vocal, whereas the copulation calls, and wahoos had better scores. The three baboons did not generalize to novel classes of CVs but generalized to novel exemplars of non-vocal sounds.

Potential factors such as acoustic similarities between the new classes of vocalizations and the grunt vocalization, and their emotional valence might explain the differences obtained among the four vocal categories.

Experiment 1.3

The aim of this experiment was to test the baboon on the same categorization task using human voices in the vocal category, and novel exemplars of non-vocal sounds in the nonvocal category. We expected that the baboons would categorize human voices in the vocal category and would generalize accurately to new exemplars of non-vocal stimuli.

Auditory stimuli

The vocal category comprised recordings from the Montreal affective voices of human voices from females and males at different ages, no specific contexts were used to classify these stimuli (e.g., female scream, enfant laughter, adult male laughter, [START_REF] Belin | The Montreal Affective Voices: A validated set of nonverbal affect bursts for research on auditory affective processing[END_REF]. The non-vocal category was a set of new exemplars of non-vocal sounds (e.g., natural, electronic).

Stimuli from both categories (22050 Hz, mono) were manually cut to 500ms duration, and played at 75dB. White noise at an amplitude of 20% was added to all stimuli to mask differences in intrinsic noise level.

Procedure

The same successful individuals were tested after finishing Experiment 1.2. Experiment 1.3 followed the same rules as the previous one. The baboons were presented with one single block of 1240 trials, corresponding to 1000 baseline trials (same as in Experiment 1.2) and 240 new trials: 120 non-vocal and 120 human vocal trials. All trials were presented randomly within the test block.

Results

Generalization of the categorization

Sensitivity and bias

We calculated d' (sensitivity) for the three subjects considering trials with novel stimuli (n=120) for the vocal category and (n=120) for the non-vocal one. Results show that again, the three baboons were less accurate in this experiment (d 'close to 0). Moreover, Mali had a negative d' suggesting that this baboon responded below chance level. Table 9. Sensitivity and response biases. This table reports the "hit", "miss", "false alarm"

and "correct rejection" rates for each of the three individuals, as well as the d'.

Interim discussion 3

Contrary to our predictions, the results reveal that human voices were not categorized as vocal sounds. Two individuals even categorized them as non-vocal sounds. The non-vocal sounds in the other hand, were accurately categorized, similar to the previous experiments. Also, d' results suggest that the individuals gave Nogo responses more often whenever they were uncertain about the presented stimuli. This hypothesis will be discussed below.

Experiment 2

The aim of this experiment was to test the three successful baboons on their ability to discriminate vocal and non-vocal sounds under different levels of added noise to each category and examine whether one of the two categories would be more (or less) affected by the noise.

Procedure

Experiment 2 used the same Go/Nogo task and general procedure, as described above. We used stimuli from Experiment 1.1, with grunt vocalizations for the vocal category and nonvocal sounds for the non-vocal one. We added increasing levels of white noise to the stimuli, ranging from 20% to 100% (complete noise) in 6 levels (20%, 33%, 50%, 66%, 80% and 100%). The baboons were presented with one single block of 2000 trials, corresponding to 1000 trials for each vocal and non-vocal condition, which were randomized and presented once and without the correction procedure. Baboons had to follow the same Go/Nogo response rule as for previous experiments regardless of the noise level.

Results

Figure 2 illustrates the variations of performance with noise level, for the vocal and non-vocal categories. It shows a similar pattern for all three subjects, with a performance decrease that especially affected the vocal category (Go) as noise levels increased, whereas performances in the non-vocal category (Nogo) remained rather high regardless of noise levels. To examine the effect of noise level (20%, 33%, 50%, 67%, 80%, 100%) and condition (vocal, non-vocal) on performance, we performed a two-way ANOVA on the mean scores for each subject. For Nekke, results showed a significant main effect of condition (vocal, non- Finally, for Mali, results showed a significant main effect of condition, F (1,1992) = 17.82, p <0.001, a significant main effect of noise levels, F (5,1992) = 7.2, p <0.001, and a significant interaction between noise levels and condition, F (5,1992) = 23.52, p <0.001. Post hoc Tukey test revealed a significant difference between the vocal and non-vocal conditions at noise levels 100% (p <0.001), 80% (p <0.001), 67% (p <0.001), and 50% (p = 0.004).

Interim discussion 4

A similar pattern was observed for the three baboons. As noise levels increased, more Nogo responses associated to the non-vocal category were performed. Within the first noise levels and until 50%, the baboons performed the categorization task accurately, suggesting that they were still able to respond based on the already learned task following the same rules.

However, as noise levels continued to increase, the baboons considered all auditory stimuli as non-vocal, because of the absence of enough vocal information that allows them to differentiate the two categories. In line with the findings of Experiment 1.3, baboons gave a Nogo response when the auditory stimuli were perceived as different from the original grunts, due to the addition of noise to these grunts. We predicted that one of the two categories would be affected by the added noise, according to the three baboon's performance, categorization of the vocal stimuli was more affected than the non-vocal stimuli. This result is in accordance with the idea that the vocal category (here restricted to grunts) is a more narrowless inclusive -category than the non-vocal one. The addition of a large amount of noise (i.e., >50%) to the grunt stimuli prevents an accurate classification of these stimuli in the vocal (i.e., grunt) category, leading to a larger amount of non-vocal (i.e., Nogo response) responses.

Discussion

The aim of the present study is to investigate the ability of baboons to categorize vocal and non-vocal stimuli. Two social groups of free-ranging baboons were tested in a computerized task, using a Go/Nogo paradigm. Throughout all experiments (see methods and results), three individuals out of the 25 baboons tested were able to perform the tasks. We will discuss the results in detail below.

Category learning and categorical performance:

In Experiment 1, we expected that the baboons would learn to categorize grunt vocalizations and non-vocal sounds and generalize to novel stimuli from both categories. Three baboons (Mali, Muse, and Nekke) succeeded all six phases of generalization in Experiment 1.1, two others succeeded the initial phases (Mako and Lomé reached P2 and P3 respectively), and the remaining 20 baboons did not achieve criterion to pass P1. They were not lacking the motivation to perform the task as reflected in the important number of trials performed.

Inspection of Table 2 shows that the three baboons learned the task at a different pace. Nekke was for instance remarkably fast to learn at P1 (only two blocks), while Muse required a little bit more block (n=6) and Mali much more (n=60). Moreover, these subjects expressed no consistent trend in the number of blocks required to reach criterion when they were exposed to an increasing number of auditory stimuli during the six training phases. For instance, Mali and Nekke required a numerically greater number of training blocks in P1 than in P6 (Mali: P1=60, P6=24; Nekke: P1=2, P6=15), but the opposite pattern was found in Muse (P1=6, P6=39). We note however that two subjects indicated a sharp increase in the number of blocs required to reach criterion in either P3 (Muse: 122 blocks) or P4 (Mali: 206 blocks).

Learning new categories in both humans and other animals generally proceeds from an initial form of rote learning to a more flexible open-ended form of categorization allowing generalization to novel stimuli [START_REF] Herrnstein | Levels of stimulus control: A functional approach[END_REF]. We assume that the peaks reported above in P3 (Muse) and P4 (Mali) correspond to the period when the strategy relying on rote learning became inefficient to solve the task with a too large number of stimuli per class

Do baboons demonstrate a vocal advantage?

We predicted that the baboons would learn faster and more efficiently the vocal category (e.g., grunts) as these calls have more ecological relevance than the non-vocal sounds.

Contrary to our expectations, Table 4 shows that the non-vocal category was acquired earlier during training than the vocal one for Nekke and Muse and required less training blocks to reach training criterion in P2, P3 and P4, and in P1 and P3 for Nekke and Mali, respectively (Table 4). These results are at odd with studies on humans that shows that participants can more easily categorize stimuli from the vocal category.

For instance, in [START_REF] Bigand | Categorization of extremely brief auditory stimuli: Domain-specific or domain-general processes?[END_REF], human participants were asked to classify stimuli in three main categories: Human voice, musical and environmental sounds at increasing durations. They found that voices were the most easily recognizable, hence higher accuracy for voices than for the two other classes [START_REF] Bigand | Categorization of extremely brief auditory stimuli: Domain-specific or domain-general processes?[END_REF]. Similarly, Suied, Agus, Thorpe, Mesgarani and Pressnitzer (2014) asked human listeners to indicate of age can also derive from variation in training. Thus, D [START_REF] Amato | Auditory matching-to-sample in monkeys ( Cebus apella )[END_REF] proposed that past exposure to visual tasks can also affect training performance in auditory tasks. In their study, they reported that the first experimental training had been with acoustic stimuli for the successful monkeys, in sharp contrast with the less successful monkeys who had previous exposures to visual stimuli. Accordingly, the group of baboons tested in the present study had significant training with complex tasks in the visual modality. Thus, it is perhaps not surprising that the youngest baboons that had less visual training in the past were the successful ones. Overall, our results suggest also that the amount of previous experience with visual/auditory modalities, and age might be crucial in auditory tasks.

General conclusion

We aimed to test the ability of Guinea baboons to discriminate vocal from non-vocal sounds, which is a core ability of vocal processing. Several playback studies have shown that Guinea baboons and other non-human primate's species can process and extract different information conveyed in their conspecific vocalizations (e.g., identity: [START_REF] Engh | Who, me? Can baboons infer the target of vocalizations[END_REF][START_REF] Miller | Individual recognition during bouts of antiphonal calling in common marmosets[END_REF][START_REF] Rendall | The role of vocal tract filtering in identity cueing in rhesus monkey ( Macaca mulatta ) vocalizations[END_REF][START_REF] Snowdon | Individual recognition of contact calls by pygmy marmosets[END_REF]Cleveland, 1980, familiarity andkinship: Cheney &[START_REF] Cheney | Recognition of other individuals ' social relationships by female baboons[END_REF][START_REF] Pfefferle | Female rhesus macaques discriminate unfamiliar paternal sisters in playback experiments: Support for acoustic phenotype matching[END_REF][START_REF] Pfefferle | Does the structure of female Rhesus Macaque coo calls reflect relatedness and/or familiarity?[END_REF], presence and nature of predators: [START_REF] Cheney | Vocal recognition in free-ranging vervet monkeys[END_REF][START_REF] Fischer | Factors Affecting Acoustic Variation in Barbary-macaque (Macaca sylvanus) Disturbance Calls[END_REF][START_REF] Fischer | Baboon responses to graded bark variants[END_REF][START_REF] Fischer | Acoustic features of male baboon loud calls: Influences of context, age, and individuality[END_REF][START_REF] Kirchhof | Functionally referential alarm calls in tamarins (Saguinus fuscicollis and Saguinus mystax) -Evidence from playback experiments[END_REF]. Until now, there is to our knowledge no behavioral evidence that they can differentiate CVs from other sounds (natural, environmental, heterospecific vocalizations etc.). In the current study, three young baboons learned to categorize grunts vocalization and non-vocal sounds. The current data suggest that subjects learned to categorize grunt and "non-grunt" sounds but failed to represent the task as a vocal vs non-vocal categorization problem. In a different perspective, the fact that only three baboons could learn to categorize auditory stimuli confirms that addressing questions on auditory processing in non-human primates is very challenging in laboratories. Similar difficulties were already encountered in past studies from other research groups. For instance, D [START_REF] Amato | Auditory matching-to-sample in monkeys ( Cebus apella )[END_REF] tested 8 capuchin monkeys (Cebus apella) in a matching-to-sample task and found that the few (n=4) successful monkeys were the youngest ones, and those who had only been trained with acoustic stimuli.

In another study, [START_REF] Stepien | Memory in Monkeys for Compound Stimuli[END_REF] trained vervet monkeys (Cercopithecus aethiops, n=7) to discriminate two auditory stimuli (pure tones), and reported that the monkeys learned the task but only after using a simplified version of the task with more positively reinforced trials. They also reported that all the monkeys were experimentally naïve. Nonetheless, the training was particularly difficult and lasted longer than the visual version of the same task [START_REF] Stepien | Memory in Monkeys for Compound Stimuli[END_REF]. Similarly, [START_REF] Kojima | Auditory short-term memory in the japanese monkey[END_REF] tested two Japanese monkeys on a Go/Nogo delayed matching to sample, and reported that the acquisition of auditory matching was more difficult, and performances were more fragile compared to the visual version of the task [START_REF] Kojima | Auditory short-term memory in the japanese monkey[END_REF]. Therefore, our findings do not depart from the literature in this respect.

The source of such difficulties remains unclear at this point, but evidence suggest that it has a multifactorial origin: the performance of the individual seems to depend on the appropriateness of the training and test protocol, the age of the subjects, and their past experience with visual and auditory discrimination problems.

Figure 1 :

 1 Figure 1: Illustration of the general timeline of the auditory Go/Nogo task.

  block of 1240 trials, corresponding to 1000 baseline trials (i.e., ten repetitions of the same 50 grunt trials and 50 non-vocal trials of P6 in Experiment 1.1) and 240 new trials including 120 non-vocal and 120 vocal trials where we presented 30 different stimuli of each of the four vocalizations' classes. All new trials were presented once randomly within the 1240 trial block. Results Three of the 25 baboons completed training after approximately two weeks. They were Muse, Mali and Nekke, i.e., the same successful individuals as in Experiment 1.1 performed 18 months earlier. Three baboons (Lomé, Mako and Lips) reached P2, and the remaining baboons did not even reach accuracy criterion in P1.

Figure 2 .

 2 Figure 2. Percentage of Nogo response for the three baboons in the vocal (triangle) and non-

  vocal), F (1,2011) = 7.16, p = 0.007, a significant main effect of noise levels, F (5,2011) = 13.05, p <0.001, and a significant interaction between noise levels and condition, F (5,2011) = 17.39, p <0.001. Post hoc Tukey tests revealed a significant difference between the vocal and non-vocal conditions at 100% (p <0.001), 80% (p <0.001), and at 67% noise level (p <0.001).For Muse, results showed a significant main effect of condition, F (1,2003) = 4.52, p = 0.03, a significant main effect of noise levels, F (5,2003) = 5.25, p <0.001, and a significant interaction between noise levels and condition F (5,2003) = 25.92, p <0.001. Post hoc Tukey tests revealed significant differences between the vocal and non-vocal conditions at noise levels 100% (p <0.001), 80% (p <0.001), 67% (p <0.001), and 20% (p <0.001).

(

  n=10 in P3 and n=20 in P4), calling for the development of more advanced (open-ended) categorical representations of the two classes. Open-ended categorization is demonstrated when the categorical responses generalize at the first presentation of novel exemplars of the classes.

  Because they did not generalize to other classes of baboon's vocalizations and categorized human voices as non-vocal sounds, we are unable to draw any definitive conclusion on their ability to process voice cue as such, and the evolution of this ability. By contrast, the accurate classification of novel vocal and non-vocal stimuli in Experiment 1.1 clearly demonstrates that the baboons have the ability to develop open-ended categories in the auditory domain.

Table 1 :

 1 The

	5	5	5

table represents the generalization procedure in experiment 1.1. It shows the increasing number of novel stimuli (vocal and non-vocal) across phases (P1 to P6).

Table 2 :

 2 Total number of trials and blocks in each phase for the 5 baboons, and mean score of

	243
	Go and Nogo trials obtained from the last 200 trials.

244

Learning process

245

For each successful individual (Nekke, Muse and Mali) we performed a one-way ANOVA 246

.75) 38 (0.84) 64 (0.75)

								14
	Muse	2 (0.4)	12 (0.8)	16 (0.8)	30 (0.75)	4 (0.8)	12 (0.6)	16 (0.8) 32 (0.71) 62 (0.72)
	Mali	3 (0.6)	8 (0.53)	15 (0.75)	26 (0.65)	5 (1)	18 (0.9)	15 (0
	Nekke 4 (0.8)	8 (0.53)	9 (0.45)	21 (0.52)	5 (1)	17 (0.85)	16 (0.8) 38 (0.84) 59 (0.69)

Table 3 .

 3 Performance at the first presentation of novel stimuli. This table reports the total number of correct responses, and their corresponding proportions, obtained at the first presentation of novel Go and Nogo stimuli in phases P4, P5 and P6. Reliable above chance performance is indicated in bold and are inferred from binomial two tailed tests (p<.05) which were computed considering the total number of novel auditory stimuli introduced in each phase and category.

* Indicate the conditions for which statistical tests were not performed due to a too small sample size.

Results show that all three baboons had an above chance performance in P6, in response to the presentation of novel stimuli in the non-vocal category. Two baboons (Mali and Muse) achieved an above chance performance in P6 in the vocal category and the remaining baboon (Nekke) remained below chance in the same condition. These data on generalization suggest that both Muse and Mali were able to correctly categorize novel stimuli from the two categories in P6, suggesting that they had developed open-ended representations of these categories. Results were less clear-cut for Nekke, as this subject showed an above chance category performance when the two categories are confounded, but a successful generalization only in the non-vocal category.

Table 4 .

 4 Number of block repetitions from P1 to P6 required to reach criterion (see main text)

		Category	P1	P2	P3	P4	P5	P6	Total
		Vocal
			2	26	9	8	3	9	57
		(Go)
	Nekke
		Non-vocal
			1	1	2	1	1	3	9
		(Nogo)
		Vocal
			16	1	13	2	6	4	42
	Mali	(Go)
		Non-vocal
			3	3	2	5	2	1	16
		(Nogo)
		Vocal
			1	2	1	2	1	3	10
	Muse	(Go)
		Non-vocal
			2	2	1	3	2	3	13
		(Nogo)

  Table6reports the scores obtained at the first presentation of each novel stimulus. Resultsshow that the three subjects generalized the non-vocal category when presented with new non-vocal stimuli. However, they did not generalize the new classes of CVs presented in the vocal category. Moreover, the "yak" was categorized as a non-vocal sound by the three subjects, as well as the "bark" by Mali and Nekke.

							Novel
			Novel classes of CVs			non-vocal sounds
		Bark	Copulation	Wahoo	Yak	Total		Total
	Subjects	n=30	call	n=30	n=30	n=120	n=120	N= 240
			n=30
	Muse	12 (0.4)	16 (0.53)	12 (0.4)	7 (0.23)	47 (0.39)	78 (0.65)	125 (0.52)
	Mali	9 (0.3)	19 (0.63)	16 (0.53)	9 (0.3)	53 (0.44)	79 (0.65)	131 (0.54)
	Nekke	8 (0.26)	16 (0.53)	10 (0.3)	8 (0.26)	42 (0.35)	79 (0.65)	121 (0.5)

Table 6 .

 6 Performance at the first presentation of novel stimuli. This table reports the total

number of correct responses, and their corresponding proportions in brackets, obtained at the first presentation of novel vocal and non-vocal stimuli. Reliable above chance performance is indicated in bold and are inferred from binomial two tailed tests (p<.05) which were computed considering the total number of novel auditory stimuli introduced in each category.

Table 7 .

 7 Table7reports the d' (sensitivity) for the three subjects, considering only trials with novel stimuli for the vocal (n=120) and non-vocal category (n=120). Results show that the three baboons were less accurate in this experiment, as d' values are roughly close to 0. Sensitivity and response biases. This table reports the "hit", "miss", "false alarm"

	Vocal category	Non-vocal category
	(Go)	(Nogo)

  Table8reports the scores obtained at the first presentation of each novel stimulus. None of the subjects had an above change generalization performance for the vocal category. By contrast, Muse and Mali were above chance for the novel non-vocal sounds.

	Mali	43 (0.35)	72 (0.6)	115 (0.47)
	Nekke	61 (0.5)	70 (0.58)	131 (0.54)
	Subjects	Human voices n=120	New non-vocal sounds n=120	Total N=240
	Muse	39 (0.32)	86 (0.71)	125 (0.52)

Table 8 .

 8 Performance at the first presentation of novel stimuli. This table reports the total number of correct responses and their corresponding proportions obtained at the first presentation of novel vocal and non-vocal stimuli. Reliable above chance performance is indicated in bold and are inferred from binomial two tailed tests (p<.05) which were computed considering the total number of novel auditory stimuli introduced in each category.

  Table 3 confirms that Mali and Muse reached this level of performance later in P6.The results were less clear-cut for Nekke. This subject demonstrated an above chance generalization performance for the non-vocal category only (P5 and P6), suggesting that Nekke achieved a narrower form of categorization compared to Muse and Mali. Altogether, our results suggest that 3 out of 25 baboons learned to categorize grunts and non-vocal sounds, and two generalized to novel stimuli from both categories.

Acknowledgements

This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program to Pascal Belin. This research was

Data availability statement

The data that support the findings of this study are openly available in Open Science Framework depository at http://doi.org/10.17605/OSF.IO/DZEF4

whether a sound was part of the voice category or the distractor category in a yes/no recognition task. They were able to detect voice stimuli from the other sound categories at very brief durations of 4ms. In the other hand, instruments were recognized at duration starting at 8ms [START_REF] Suied | Auditory gist[END_REF]. The advantage for the non-vocal category obtained in baboons could be explained by the nature of our two categories of stimuli. In Experiment 1.1, the vocal category remained a narrow category limited to grunt vocalizations, while the nonvocal category was much more heterogeneous (composed of environmental, natural, and electronic sounds). This asymmetry between the two categories could have favored a faster opening of the non-vocal category during training, in comparison to the vocal one. This hypothesis was examined in Experiment 1.2 and discussed next.

Categorization of new auditory stimuli: Novel CVs' classes and human voices

Before experiment 1.2, we exposed again our baboons to P1 to P6 as a preliminary phase after 18 months break. This replication confirmed the reliability of our findings, as the same three individuals succeeded once again, while the remaining individuals failed again. Presentation of new classes of conspecific vocalizations (Wahoo, bark, yak, and copulation call) and new non vocal sounds in Experiment 1.2 confirmed that the non-vocal category was the only openended category allowing generalization to new stimuli (see Table 6). The lack of generalization to novel vocal sounds suggests that these baboons have developed in Experiment 1.1 a strategy involving two categories, the grunts and only the grunts on the one hand, and the other sounds on the other hand, and then kept this strategy when they were presented with novel vocal stimuli in Experiment 1.2. In other words, they solved the tasks by touching the screen (Go-response) in case of grunts and not touching the screen (Nogo) in 8).

Results of Experiment 2 further support the conclusion that the baboons relied on a strategy distinguishing the grunts from the non-grunt stimuli. This experiment revealed a similar pattern in our three baboons: their performance in the non-vocal category did not considerably vary as noise levels increased, but their performance in the vocal category decreased to chance level (as early as 50% of noise) or below when the noise level increased (Figure 2). These results suggest that the vocal category did not resist the added white noise. When baboons could not process enough features to detect the grunt vocalizations, because of the white noise, they categorized them as "non-grunt" and gave a Nogo response, in accordance with grunt vs non-grunt categorization strategy.

Age and past-experience factors

We note that Nekke, Muse and Mali are the youngest subjects of our group (3-years old for Nekke, 4 for Muse and Mali), suggesting a possible effect of age in our study. In support of this idea, 2 other young baboons were successful in the first phases of Experiment 1.1, Lomé (5 years old) passed criterion from P1 to P3 but did not achieve criterion to P4, and Mako (4 years old) passed criterion from P1 to P2 (see Table 2). Similar results were reported in other studies involving monkeys. For instance, [START_REF] Amato | Auditory matching-to-sample in monkeys ( Cebus apella )[END_REF] could only successfully train 4 out of 8 Tufted capuchin monkeys (Cebus apella) in an auditory matching-to-sample task. These four monkeys were aged between 4 and 6 years old, whereas three of the remaining monkeys were considerably older (14, 21 and 22 years old). They suggested that age might be a factor in auditory experiments. This age effect could lie in perception. In humans, the younger individuals have a better auditory acuity than older individuals, who suffer from audiometric loss indicated by thresholds elevation [START_REF] Humes | Speech-recognition difficulties of the hearing-impaired elderly: The contributions of audibility[END_REF] and general slowing in auditory processing [START_REF] Alain | Effects of age and background noise on processing a mistuned harmonic in an otherwise periodic complex sound[END_REF]. Although not tested, these effects conducted at Station de Primatologie-Celphedia, CNRS UAR846, Rousset, France. The authors thank its staff for technical support.

Recognition of very short sounds from timbre cues. The Journal of the Acoustical Society of America, 135(3), 1380America, 135(3), -1391America, 135(3), . https://doi.org/10.1121/1.4863659 /1.4863659