Christophe Lerot

Thomas Danckaert

Michel Van Roozendael

Robert Spurr

Diego Loyola

Melanie Coldewey-Egbers

Mariliza Koukouli

Irene Zyrichidou

Dimitris Balis

Jean-Christopher Lambert

Extension of the ESA CCI Total Ozone Climate Data Record with the Application of the GODFITv3 Algorithm to OMI Observations

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The OMI _GODFIT product: first validation results

The GODFITv3 algorithm 1  Direct fitting of measured radiances in the Huggins bands (325-335 nm).  State vector includes a.o. total ozone, effective temperature and albedo.  O 3 cross-section: Brion, Malicet, Daumont et al. 2

1 Lerot et al., JGR, 2014;2 Malicet et al, J. Atmos. Chem., 1995, 3 Mc Peters et al., JGR, 2007;4 Ziemke et al., ACP, 2011; The OMI _GODFIT product: inter-sensor comparison Extension of the ESA CCI total ozone climate data record with the application of the GODFITv3 algorithm to OMI observations C. Lerot (1,) , T. Danckaert (1) , M. Van Roozendael (1) , R. Spurr (2) , D. Loyola (3) , M. Coldewey-Egbers (3) , M. Koukouli (4) , I. Zyrichidou (4) , D. Balis (4) , J.-C. Lambert (1) , J. Granville (1) , F. Goutail (5) , J.P. Pommereau (5) , and C. Zehner (6) (1) BIRA-IASB, (2) RT Solutions, Inc., (3) DLR-IMF, (4) AUTH, (5) LATMOS, (6) ESA/ESRIN, : christl@oma.be

Abstract

One of the great achievements of the first phase of the ESA Ozone Climate Change Initiative has been the release of new level-2 total ozone data sets from GOME, SCIAMACHY and GOME-2/Metop-A reprocessed with the latest GODFIT-3 algorithm. These data sets are characterized by unprecedented standards of consistency, stability and accuracy.

Recently, GODFIT has been adapted in order to analyze backscattered light spectra measured by the Ozone Monitoring Instrument (OMI). In particular, a new look-up table version of GODFIT has been developed to process the massive OMI level-1 data set in a timely and fast manner. This approach maintains a level of accuracy similar to that of the on-line GODFIT version, in addition to providing accelerated performance by a factor of 10. This fast algorithm has been used to reprocess the entire OMI time series, thus extending the ESA CCI Climate data record for total ozone. Here, we compare the OMI GODFITv3 total ozone product to the two operational products OMI-TOMS and OMI-DOAS, and also to the historical multi-sensor CCI data set.

We show that an optimization of the pre-flight instrumental slit functions has been necessary in the ozone fitting window (325-335 nm) in order to completely eliminate any row dependence in the product. Such an optimization of the pre-flight slit functions has also the potential to enhance the consistency between total ozone data sets from different sensors as demonstrated here with GOME-2A and -2B.  A priori profiles: total column-classified climatology TOMSv8 3 combined with the tropospheric column climatology from OMI/MLS 4  Simulated spectra are computed on-the-fly with LIDORT or extracted from pre-calculated tables. The latter option is faster by a factor of 10, while maintaining a very high level of accuracy.  Optional Brewer-based soft-calibration of level-1 reflectances to reduce the impact of calibration limitations. This procedure is not necessary for OMI.

ESA CCI total ozone data sets

LUT -online difference (%)

Main features

GODFIT has the capability to optimize instrumental slit functions and to account for resolution change along the orbit. This may impact significantly the product quality.

Optimization of instrumental slit functions

Nadir-normalized ozone differences (%)

Row dependence of the OMI data set 2. GOME-2B/-2A consistency Mean GOME-2B-GOME-2A differences

Conclusions

 The algorithm GODFITv3, selected to generate the total ozone ECV as part of the Ozone_CCI project, has been adapted to handle the OMI data. The full mission has been recently reprocessed using a fast look-up table version of the algorithm and is available to the public on www.esa-ozone-cci.org, along with the other CCI data sets.  A proper characterization of the instrumental slit functions is essential to have products of good quality and may help to enhance the inter-sensor consistency. In particular, using an optimized OMI slit function in the O 3 fitting window led to a significant reduction of the row dependence in the OMI_GODFIT product.  In general, the three total ozone products OMI_GODFIT, OMI-TOMS and OMI-DOAS are very consistent. However, larger differences appear in extreme conditions (high SZAs, extreme O 3 columns, high clouds, …). Also, OMI_GODFIT agrees very well with the other CCI data sets as well as with the US golden standard long-term record SBUV v8.6.  Validation shows that the time stability of GODFIT-OMI is excellent (<0.5%/decade) and meets the CCI user requirements. In future CCI activities, this product will be used as the long-term reference for soft-calibrating other sensors.

  data GTO-ECV v2 * * Coldewey-Egbers et al., AMTD, 2015

  Comparison with standard climate total O 3 records CCI Level-2 data sets Zonal mean differences in (60°S-60°N) Total O 3 differences wrt SBUV v8.6 (%) More details provided in Poster #89 by M. Koukouli et al. Time dependence SZA dependence


Bertrand Fréchède

Erwan Jolivet

Christophe Lecomte

Thomas Lemaire

Philippe Petit

Philippe Beillas
email: philippe.beillas@ifsttar.fr

A public-private integrated framework for in-silico virtual testing: the open-source PIPER project to personalize and position HBMs

Introduction

As shown by the main topic of the VPH2016 conference, 'Translating VPH to the clinic' is now a critical objective of the VPH research community. At the same time EuroNCAP, the European consumers' association for vehicle safety rating, very recently introduced the use of Human Body Models (HBM) simulations to define some of the test conditions in their consumer test protocols for pedestrian impact scenarios. Although they may at first seem somewhat unrelated, such different research areas as the clinical and automotive crashworthiness research benefit from the same advances in numerical methods and computational capacity for imaging and modelling. Further, both research communities may share very similar challenges, not only in their approach to the modelling of the biomechanical behaviour of the human body, but also in their need to transfer this research into tools that will effectively prove their societal benefits in a safety and regulation constrained socio-economic environment. With relatively similar aims and scopes, expectations in the field of in-silico clinical trials, or virtual testing, are high in both industries. The PIPER project is a 3.5 year EC funded research project that aims to deliver tools that will allow a user to personalise and position any Finite Element (FE) HBM, in a virtual vehicle environment, for virtual testing. The challenge is scientific, as state-of-the art methods had to be designed and implemented to e.g. develop Statistical Body Shape Models (SSM) from clinical imaging data, or to ensure that deformed FE models would still meet solvers' quality metrics for simulations. The promotion of such tools was identified as critical and strong choices (open-source, meta-toolsindependent from the FE model/code, contribution of the end-users to all stages of the project) were made to ensure a successful public-private research partnership. This abstract presents some of these key aspects, with an emphasis on their possible usefulness and transfer to the VPH community.

Materials and Methods

In the automotive application context that is presented here, personalizing an existing FE HBM model means scaling and deforming it towards dimensional and shape targets. Positioning a FE HBM model means being able to reposition its segments towards a target posture. The THUMS1 or GHBMC2 models are state of the art examples of such FE HBMs for crash applications. While very detailed, they correspond to a limited set of specific subjects' dimensions, properties and to limited postures. Two main complementary software components form the core of the PIPER tools (Cf. Figure 1): first, the statistical predictor tools provide a description of body shape and/or position from few user inputs (age, height, positional constraints…) and a-priori knowledge regarding physiology, biomechanics and ergonomics. This a-priori knowledge was acquired from either the literature, existing datasets, or from newly generated information. Merging information from various datasets to generate a statistical model of the whole body is the main scientific challenge for this task. The second software component is composed of personalizing and positioning modules that rely on a range of near real time geometry-and physics-based numerical methods, to transform the FE HBM towards targets that describe the desired position or body shape determined by the user and the first component. The challenges are to combine these constraints of targets and user inputs with the functional constraints of FE HBMs (contacts, kinematics joints…) and to ensure that FE quality metrics are met to permit the FE simulation. Statistical predictor tool (a-priori knowledge) Several anthropometric measurement databases are available for a wide range of populations (e.g. ANSUR, NHANES, …) and regressions of anthropometric measurements were generated using previously published methods [1]. Mean or sampled anthropometric measurements for specific subjects (a given BMI, age, etc.) from a chosen population can therefore be predicted and used to define the target dimensions for personalization tools. However, these targets mainly define the external body shape. The internal geometry is based on a statistical model of the whole skeleton built upon a SSM approach. In order to build SSMs, a set of population samples geometries, e.g. as result of 3D imaging techniques, has to be gathered and a reference mesh needs to be defined and registered on the population samples to perform the statistical analysis. Publicly available segmented data for full bodies is however limited, and segmentation or model based registration usually requires much manual work using a dedicated software. The AnatoReg software is a semi-automated registration solution that was developed by the Anatoscope3 company, a spinoff from our INRIA partner, during the project. It is based on advanced interactive model-based registration algorithms used to generate a subject's geometry from a whole body CTscan. Based on sampled landmarks or surfaces meshes evaluated on subjects, the variability of shapes, in particular those of the skeletons, was then expressed in terms of SSMs. As shapes of bones are independent of posture, their SSMs were evaluated through bone by bone partial Procrustes analysis [START_REF]Statistical shape analysis[END_REF], before analysing the mean, variance, and covariance of the whole skeleton shape. The shape SSMs can provide surface mesh targets either from the shape means or from sampled virtual subjects. Providing the statistical description of the whole body will finally be addressed by linking the above presented anthropometric measurement databases, which are available for a much wider range of populations, to the surface shapes. Regarding positioning, defining predictors can have many advantages for the user where a physiologically likely position is needed but would be either unknown or too tedious to define by hand. An example concerns the predictive positioning of the spine where a-priori knowledge is provided by the combination of different methods. An inverse kinematics articulated linkage model of the skeleton [3] forms the backbone to the predictive positioning tool, allowing to solve for a user-defined target body posture under constraint of e.g. vehicle environment. Postural task-related linear regressions were then calculated on a set of parameters defining a 3D spline based on both in-vitro and in-vivo bending data to refine the postural prediction of the spinal segment. Thus in the positioning step, e.g. the pelvis and head position and orientation may be used as input to the spine predictor. The predicted output spline will then be used to constrain the position of the articulated vertebra of the PIPER model, by minimizing the distance between the model's vertebras and their counterpart positions on the spline. Personalizing and positioning tools In order to apply the above defined predicted targets (shape and/or position) on any FE HBM regardless of its FE format, a customizable FE parser was implemented to describe specific FE format rules to interpret the model into a custom PIPER format. A set of metadata (defined only once by the user, for a given FE model) is also required to describe the anatomy of the FE HBM and its functional constraints. From the model geometry and its metadata, a simplified physics model is built, composed of bones and the flesh. The bones are articulated from the robotic joints defined either in the metadata or from anatomical landmarks pointed on the HBM in the metadata. Joints may also be defined using a combination of sliding contacts and generalized spring between bones to achieve more complex kinematics. Once imported, the user can interactively define say positional constraints using a real time light-weight physics simulation. The physics model accounts for bone collisions so that the range of motion of the HBM respects its bone geometry. The flesh is simulated as a uniform material using a frame based approach developed in the computer graphics community [START_REF]Deformation models: tracking, animation and applications[END_REF]. The resulting system of equations, comprising the physics material law, the joints and contacts constraints as well as the userdefined positioning targets, is efficiently solved using a mixed stiffness/constraints approach [START_REF] Tournier | Proc SIGGRAPH[END_REF] implemented in the Sofa simulation framework 4 . This simulation respects the main functional constraints defined in the FE HBM. To obtain a realistic target position, the ergonomic and biomechanical constraints defined by the predictor tools will then be considered in the simulation as soft constraints. The same approach is offered to the user regarding the personalization to combine model requirements and statistical predictions of shapes. To finally transform the FE model towards thus defined targets, a set of numerical methods have been implemented following a benchmark study regarding their performance with regard to the precision of the transformation, the impact of the transformation on mesh quality and the respect of FE functional constraints (i.e. the model can be run in a FE simulation) [START_REF] Jolivet | [END_REF]. Regarding personalization, the dual Kriging interpolation was implemented as it was shown that in such application the key factor is how the target is defined rather than the interpolation scheme. For positioning, two different approaches were implemented. The first one is a more precise version of the above mentioned physics simulation, which computes a deformation field in the whole space. The second one is a contour-based geometrical approach that uses specific metadata to take into account joint specificity [7]. A set of tools were finally implemented to improve the realism of the body shape and the FE mesh quality after a transformation.

Results

Statistical predictor tool (a-priori knowledge)

The AnatoReg tool has so far permitted the prototypical semiautomatic segmentation of the thorax and lower limb bones of 25 Post-Mortem Human Subjects (PMHS), with a usertime of roughly an hour per subject. Skeletons of over one hundred PMHS whole body CTScans are planned to be segmented; these models as well as the manually segmented reference models will be made publicly available. The data will include both surface meshes and an extended set of anatomical and anthropometric data used to identify joint coordinate systems and articulate the skeleton. The developed a-priori anthropometric tools based on [1] first permit to generate virtual populations and then to evaluate regression models between any chosen predictors and measurements of interest. For example, from the ANSUR data [8], one can generate a virtual population with a given male-female ratio, generate the regressions between the BMI and stature predictors on one hand and external body measurements on the other hand. Shoulder breadth and thigh circumference can then for example be predicted for subjects having a stature of 1.65 m and a BMI of 23. Tools to generate the SSMs can be applied to all the parts of the segmented body (the skeleton at this stage) or to part of it. One can then predict the mean shapes or sample some of them, while considering correlation between them. For example, one can sample whole sets of vertebrae such that the dimensions of all of them are consistent. Another possible use of the SSMs is to calibrate their modal weights to match as closely as possible a specific subject. Regarding the positioning, a first version of the inverse kinematics tool and spine posture predictor have been implemented to predict apriori postures in the sagittal plane.

Personalizing and positioning tools

The PIPER model can be interactively manipulated to position the HBM through a dedicated GUI (Cf. Figure 3). Users also have access to many positioning parameters such as joints angular values (ex: hip flexion), bone orientation (ex: pelvis tilt), bone position (ex: head or hand position). Further to the evaluation of the FE model transformation [START_REF] Jolivet | [END_REF], the personalization and positioning workflow have been evaluated on a range of end-user defined application scenarios by quantifying for FE quality metrics after transformation. The link between the statistical predictor tools and personalizing and personalizing is not implemented yet and will be the main milestone for the last year of the project.

Discussion and Conclusions

This abstract presented a general framework as well as some key methods and tools that will be delivered for personalizing and positioning FE HBMs at the end of the PIPER project (April 30, 2017). The scope of the project is ambitious on several aspects. On the scientific side, some new developments have been proposed [START_REF] Jolivet | [END_REF][START_REF] Moreau | Proc ESB[END_REF] to e.g. answer the need for quality control of geometric deformation and quick and efficient automatic model registration. On the project design and management side, the requirement of transferring tools to the industry resulted in implementing some novel approaches to involve both public and private partners (and thus, the end-user) at all stages of an EC funded project. This included a questionnaire to users, case-study application scenarios, versioning of tools' deliverables and users' beta-testing. Strong design choices were made by imposing a fully open-source framework for all tools and databases to be released and by aiming at a meta-tool, i.e. not FE model/code dependent. An open-source design meant a strong change in cultural paradigm for our industrial partners, but also proved an incentive to federate new contributions to the project and aimed at ensuring that a users' community will continue to build up and feed the tool and its databases after final delivery. The applications presented in this abstract target the automotive industry. However it is to be strongly emphasized that the PIPER tools are by design not restricted to be used in this field of application. Some of them, like SSM or Kriging/RBF approaches were already used or even initiated by the VPH community for clinical applications. Some, such as the anthropometry tool and database, may be of interest to different publics, and will be released as fully standalone options or separate software. The AnatoReg software allows for quick personalized surface model building and registration from medical imaging and the a-priori knowledge toolbox may be used to account for subjects' variability when designing parametric studies or sensitive analysis for numerical verification [START_REF] Viceconti | [END_REF]. « Accurate models produce predictions that are consistent with the experimental observations under a wide range of conditions » [START_REF] Viceconti | Figure 3: PIPER GUI with an example of FE model before and after positioning[END_REF] and being able to model the human's physiological variability appears to be key if we wish to have in-silico clinical trials accepted by the regulatory experts and policy makers. Finally, the personalizing and positioning tools may be used for personalization of existing complex segmental FE models. From the authors' perspective, these tools open new perspectives in a broad range of FE modelling applications and have the potential to significantly facilitate already initiated subject-specific modelling processes in the VPH community.

Figure 1 :

 1 Figure 1: The PIPER workflow

Figure 2 :

 2 Figure 2: AnatoReg model based registration examples; pelvis SSM first two modes with ± 2.0 'sigma' deviation

http://www.lstc.com/thums

http://www.ghbmc.com/

www.anatoscope.com

www.sofa-framework.org

Acknowledgements

This research has received funding from the European Union 7th Framework Programme ([FP7/2007-2013]) under grant agreement 605544 [PIPER, http://piper-project.eu].


est destinée au dépôt

  Seismic stratigraphy of Cretaceous eastern Central Atlantic Ocean: Basin evolution and palaeoceanographic implications Yannick Mourlot, Gérôme Calvès, Peter D. Clift, Guillaume Baby, Anne-Claire Chaboureau, François Raisson To cite this version: Yannick Mourlot, Gérôme Calvès, Peter D. Clift, Guillaume Baby, Anne-Claire Chaboureau, et al.. Seismic stratigraphy of Cretaceous eastern Central Atlantic Ocean: Basin evolution and palaeoceanographic implications. Earth and Planetary Science Letters, 2018, 499, pp.107-121. 10.1016/j.epsl.2018.07.023. insu-01857493


Diemo Schwarz
email: schwarz@ircam.fr

Daniel Artur Schindler
email: daniel.schindler@heardis.com

Severino Spadavecchia
email: rino.spadavecchia@heardis.com

A Heuristic Algorithm for DJ Cue Point Estimation

This article treats an aspect in a larger research agenda to understand DJ practices, which are an important part of popular music culture: We present a heuristic algorithm that estimates cue points where tracks should cross-fade in a DJ mix. We deduced statistics and heuristics from a list of rules provided by human experts, and from a database of example tracks with given cue regions. We then created an algorithm for cue-point estimation based on rich automatic annotations by state of the art MIR methods, such as music structure segmentation and beat tracking. The results were evaluated quantitatively on the example database and qualitatively by experts.

INTRODUCTION

DJ techniques are an important part of popular music culture but are not very frequently the topic of scientific research [START_REF] Schwarz | Towards Extraction of Ground Truth Data from DJ Mixes[END_REF]. This article treats one aspect in a larger research agenda to understand DJ practices. The outcomes from such an understanding are many, for instance musicological research in popular music, cultural studies on DJ practice and reception, music technology for computer support of DJ'ing, automation of DJ mixing for entertainment or commercial purposes. We focus in this article on the automatic estimation of cue regions (see figure 1), i.e. the points in the source audio tracks of a DJ mix, where two tracks should cross-fade.

The work is part of the ABC DJ EU project 1 within which an automatic track annotation and DJ mixing algorithm is to be developed in the context of audio branding for in-store music delivery. It is based on input provided by one of the project partners HearDis!2 , an agency for audio branding. Their music experts provided heuristic rules (section 3.1) and an example database of tracks with cue-points (section 3.2). The rules were then verified with the examples (section 3.3) and those rules that could be realised computationally were implemented in a prototype automatic annotation software (section 3.4). The software was run on the example tracks and the results evaluated computationally and by human experts (section 3.5). Their feedback gave rise to adaptations in the algorithm, which improved the quality of the annotations (section 3.6). Thus, the article is structured to give an account of the informal iterative design process that allowed to keep the end-users closely in the loop. Also note that the project context is not DJ mixing for clubs or performance, but point-of-sale (PoS) automatic mixing in shops, based on semi-automatic music annotation and generated playlists. However, the automatically produced mixes should retain a certain DJ quality (beat synchronicity, cross-fades).

RELATED WORK

There is quite some existing work on tools to help DJs produce mixes [START_REF] Cliff | Hang the DJ: Automatic sequencing and seamless mixing of dance-music tracks[END_REF][START_REF] Fujio | A system of mixing songs for automatic DJ performance using genetic programming[END_REF][START_REF] Ishizaki | Fullautomatic DJ mixing system with optimal tempo adjustment based on measurement function of user discomfort[END_REF][START_REF] Aspillaga | Mixme: A recommendation system for DJs[END_REF][START_REF] Molina | Beatjockey: A new tool for enhancing DJ skills[END_REF][START_REF] Davies | Automashupper: Automatic creation of multi-song music mashups[END_REF], bit much less regarding annotation and information retrieval from audio tracks or recorded mixes. The first works opening up research on DJ-related information retrieval are on constitution of playlists [START_REF] Kell | Empirical analysis of track selection and ordering in electronic dance music using audio feature extraction[END_REF], segmentation of mixes [START_REF] Glazyrin | Towards automatic content-based separation of DJ mixes into single tracks[END_REF][START_REF] Scarfe | Segmentation of electronic dance music[END_REF], and identification of tracks within a DJ mix by fingerprinting [START_REF] Sonnleitner | Landmarkbased audio fingerprinting for DJ mix monitoring[END_REF]. The latter team also produce an extensive database of ground truth annotations of playlists with approximate start and stop times of tracks on a large number of Creative-Commons licensed mixes made from open-licensed dance tracks published on the Mixotic net label. 3 However, that database was not aimed at and is not precise enough to give information about cue points.

Schwarz and Fourer [START_REF] Schwarz | Towards Extraction of Ground Truth Data from DJ Mixes[END_REF] introduced a larger framework of DJ music information retrieval, where the tracks constituting a DJ mix are sample-aligned to the recorded mix in order to be able to separate the source tracks and estimate the volume fade curves and cue-points. They published an openly available database of artificial but realistic DJ mixes with the necessary ground truth of their construction [START_REF] Schwarz | A dataset for DJ-mix reverse engineering[END_REF], based on the tracks collected by Sonnleitner et. al. [START_REF] Sonnleitner | Landmarkbased audio fingerprinting for DJ mix monitoring[END_REF]. Kim et al.'s approach of highlight detection [START_REF] Kim | Automatic DJ mix generation using highlight detection[END_REF] chooses segments from a collection of tracks that "stand out" as determined by a convolutional recurrent attention network, and cues them beat-synchronously.

Our work is heavily based on music structure estimation from audio, see [START_REF] Paulus | State of the art report: Audio-based music structure analysis[END_REF][START_REF] Peeters | Deriving musical structures from signal analysis for music audio summary generation: "sequence" and "state" approach[END_REF] for an overview of existing methods. We use the method described in [START_REF] Kaiser | A simple fusion method of state and sequence segmentation for music structure discovery[END_REF] that fuses the representation of a piece of music as a succession of states with similar and acoustically homogeneous content (separated by peaks in a novelty function), and the approach aiming to detect the repetition of sequences in the music. The novelty function is computed by convolving a selfsimilarity matrix with a checkerboard kernel [START_REF] Foote | Visualizing music and audio using selfsimilarity[END_REF].

CUE POINT ESTIMATION

Cue points define the regions where tracks in a DJ mix fade in or out to blend with other tracks (see figure 1). DJs will usually choose them by hand according to the context of the current DJ set, based on their experience and familiarity with the specific track. However, when computer support or automation of DJ mixing is called for, we have to devise heuristics and an algorithm that can analyse the music content of a track in order to come close to the human decision.

In our context of PoS automatic mixing, the automatically estimated cue region proposition saves time for the human annotators of new tracks to be included in new automatically generated playlists, who only have to verify the automatic annotation and correct it if necessary.

Human Expert Rules

In order to get a high-level framing of the problem of cue point estimation from the point of view of the users, project partner HearDis! provided the content-and context-based criteria in table 1 for the choice of cue regions for the aim of PoS automatic mixing. The concern in that case is to decide if the song can start immediately, or if the intro has to be shortened, possibly because it is a prolongued club DJ-friendly version, and if the end has to be shortened because in-store music needs to change more often than club music in order to achieve a higher level of variety. We can already see that many of these points are dependent on audio and musical content (repetition, presence of voice) and even cultural context (what is too noisy or nonmusical?). The key point of the ensuing work was to find out which of these criteria were computationally feasible with the current tools, and whether the rate of errors with regard to the unfeasible criteria not modeled in our algorithm was acceptable.

Ground Truth Database of Cue-Points

HearDis! provided a set of 30 example tracks in MP3 format, each in two versions:

the track shortened according to human-decided cuein and cue-out regions with fades applied to them

We then annotated the start and end points of the cut regions, and the durations and kinds of fades or cuts by hand in text label format as produced by AUDACITY4 . This does provide example cases of shortening and fade times. The results of a statistical analysis of the annotations are given in tables 2-3. Statistics of cue region durations are always given with the zero-duration regions removed, since they correspond to "cut" transitions (no cross-fade).

Furthermore, the examples revealed other content-based decisions, such as, in one track, removing one repetition of the exposition of a synth line by cutting the intro in half, or removing redundancy in long end parts of songs.

Comparison of Ground Truth Cue Points with Music Structure Analysis

The audio-based music structure analysis algorithm [START_REF] Kaiser | A simple fusion method of state and sequence segmentation for music structure discovery[END_REF] divides a piece of music into its significant parts, and organises them into classes (e.g. corresponding to intro, outro, chorus, verse). This is done on multiple-levels, where, from the lowest to the highest level, the structural segments are fused into larger classes.

Our hypothesis was that the intro and outro segments would stand out and could be a good basis for cue regions. We verified this by calculating two versions of automatic structural analysis, using the tool IRCAMSUMMARY, on the full length example tracks. The first version, state mode [START_REF] Kaiser | A simple fusion method of state and sequence segmentation for music structure discovery[END_REF], is based on a fusion of homogeneous state and sequence repetition segmentations. The second version, NSMF mode [START_REF] Kaiser | Music structure segmentation[END_REF], uses non-negative matrix factorisation (NMF) of similarity matrices as a mid-level representation to classify the structure. This mode is called NSMF for Non-negative Similarity Matrix Factorization. We then compare the structural segments with the human suggestions of cue regions. The plots in figures 2 and 3 show the ground truth cue regions of the example tracks overlaid with multi-level structure analysis regions. Each level's segment boundaries are shown on one horizontal line as coloured dots, from lower levels in violet to higher levels in cyan, which proceed by fusing lower-level segments. Right-pointing triangles show the cue-in fade region, or cut, when only one triangle is visible (length of zero). Left-pointing triangles show the cue-out region and end point of the full length example. Tracks are sorted by length, for easier observation of maximum final track length.

These plots reveal that, first, the lowest (most detailed) level state mode summary in figure 2 is more pertinent, since it has a segment structure better coinciding with the annotations (it is also beat-synchronous, unlike the NSMF mode summary in figure 3), and, second, that almost half of the songs were not shortened at the beginning:

• 14 cue-in start points are cuts at song start • 16 cue-in start points are within the first structure segment

• only 3 cue-in segments are longer than 1s 5 5 The frequent presence of cuts instead of fade regions are due to the fact that the existing PoS playout system at HearDis! does not yet do cross-fade mixing, so that the experts are trained to find cue points where tracks can cut from one to the next.

• the cut-off point for long tracks is mostly between 5:30 and 6:00, with 3 tracks going until 7 minutes

Cue Point Estimation Heuristic Algorithm

The first proposed algorithm detailed below is solely based on the song structure estimation by the IRCAMSUMMARY module, which determines significant parts of the track, including the intro and outro sections which are used to place the cue regions. For this first iteration, we wanted to see how far we would come only with song structure information, without taking the audio content into account.

The above observations from the example tracks suggest the following heuristic algorithm for the cue region estimation (always of 10 s length):

1: function CUE-IN 2:
return cue region such that its end coincides with the end of the first long enough (10 s) structural region at the lowest level 3: end function

1: function CUE-OUT 2:
if the song is not too long (<= 6 min) then for long songs, we apply the explicit rule to shorten songs that are too long (see 3.1)

6:
if there is a structural segment longer than 10 s in the time span between 5:30 and 7 minutes 67:

then 8:
return the cue-out region placed at its start end if 13: end function

First Evaluation of Cue-Point Detection

We created cue region estimations for the tracks in the cuepoint example database. To facilitate evaluation, we also exported the example tracks faded at the estimated cuepoints. These examples were evaluated by music annotators at HearDis! in order to validate the heuristics. (But keep in mind that the cue-point estimation is always only a starting point for a human annotator who solely is in the position to correctly judge the content and context of the tracks.)

Nevertheless, the numerical comparison between the estimated cue points and the hand-annotated ground-truth cue points from the test database in figure 4 shows that over 50% of estimated cue points are within 10 seconds from the manual choice and for two thirds of the examples the absolute time differences are smaller than 20 seconds. The two outliers with cue end estimation differences over 60 s are due to a more flexible interpretation of the shortening rule by the human annotators (some tracks were left at much longer than the cutoff rule of 6 minutes). A subjective but systematic evaluation of the automatically faded example tracks was carried out by the human music experts at HearDis!. They provided precise feedback in the form of screenshots with the 3 versions of each track aligned (original, human-cut, automatically cut) and remarks for the problematic cases (see figure 5). The feedback was positive about the algorithmic choice of cue points, with the remark that, for PoS applications, it is always OK to cut more than a human annotator at beginning and end. There were only 5 problematic cases, listed in table 4. The remarks show the limits of our simple algorithm, where the human decision mobilises deep contentand context-dependent knowledge up to the cultural level (e.g. that applause is a special noise that marks the end of a performance).

Second Iteration

Based on the above feedback, we chose to implement the only computationally feasible content based criterion for cue-in/cue-out estimation of loudness. From an analysis of the example tracks, we could determine that a threshold of -9 dB relative to the max loudness of the track catches all the cases where an intro or outro had been considered as too quiet, without introducing false positives. 7We then shift the cue region until its minimum loudness is larger than -9 dB relative to the max loudness of the track. Loudness of a segment is calculated as the max peak RMS energy in 2s windows.

The second evaluation results, shown in figure 6, slightly improve the difference between estimated cue-regions and manual choice, and has been approved by the human experts.The found cue points are also shown in figures 2 and3.

Musically, the decision to place the cue-in region at the end of a song-structure region work very well, since at the end of the fade in of the new track, just when it has reached full volume, and the previous song has just vanished, there is a clear change in content (as predicted by the song structure), that catches the ear and clearly signals the start of the track.

CONCLUSIONS

We presented a heuristic algorithm to estimate cue points for generating DJ-like mixes based on automatic annotations by state of the art MIR methods of music structure segmentation, coupled with domain knowledge of human experts, and backed by a database of example tracks. The iterative design process created a close feedback loop between researchers, developers, and expert users, quickly reaching a satisfactory solution for their specific needs of in-store music playout and audio branding.

The next step we will take is to determine fade-in and fade-out times relative to the beat positions of the track, as estimated by the beat marker annotation of the IRCAM-BEAT module. The cue regions would last for 4 measures, and it is straightforward to adapt the algorithm to search for the beat position closest to a structural boundary to anchor a cue region.

In future work, we could examine which of the criteria in section 3.1 are possibly detectable by content descriptors and classifiers available in MIR research, e.g. voice detection, or develop specific descriptors and classifiers for the "music-ness" of audio. However, this would need many more annotated tracks to train the method. Before this effort is made, feedback should be gathered about the number of problematic cases in real-world usage of the existing system.

Figure 1 .

 1 Figure 1. Cue points and cue regions with the resulting volume fade curve for one track in a DJ mix.

3 :

 3 return start of the last structural region that is long enough (10 s) as cue-out start 4:

  points cue points estimated by state+loudness

Figure 2 .Figure 3 .

 23 Figure 2. Ground truth cue regions of the example tracks (black) overlaid with IRCAMSUMMARY multi-level structure analysis regions in state mode (violet to cyan dots), and cue-points estimated by final algorithm (red).

Figure 4 .

 4 Figure 4. Histogram of the time difference between handannotated ground-truth cue points and cue points estimated based on state mode.

Figure 5 .

 5 Figure 5. Example of subjective feedback: The human expert hand-aligned the original track (bottom), the manually cut and faded track (top), and the automatically cut and faded track (middle) to evaluate and comment on important differences.

Figure 6 .

 6 Figure 6. Histogram of the time difference between handannotated ground-truth cue points and estimated cue points with loudness criterion.

Table 1 .

 1 Track is too long in general (more than 6 to 7 minutes) 2. Intro is too repetitive (especially DJ-friendly versions) 3. Intro is too quiet for too long (more than 4 to 8 beats) until track is of discernible loudness (at the PoS). Ex-Expert-provided criteria for choice of cue regions.

	segment	mean / median mean / median / number
		start time	of non-zero durations
	cue in	13.6 / 8.9 1.3 / 0.9 / 23
	cue out	290.0 / 316.7 8.4 / 9.2 / 4
	track end 369.1 / 363.0 n/a

1. the full length track 1. ception: Artist is already singing 4. Intro is too noisy/non-musical 5. Outro is too repetitive 6. Loudness drops significantly but outro lasts longer than 4 to 8 bars. Exception: Artist is still singing 7. Outro is too noisy/non-musical 8. Generally silence at the beginning and end of a track should be shortened to a minimum

Table 2 .

 2 Statistics of start time and duration of ground truth cue regions for the 30 example tracks in seconds.

Table 3 .

 3 Statistics of minimum/maximum start and duration of ground truth cue regions for the 30 example tracks in seconds.

	segment	min / max	min / max	min / max
		start time	end time	non-zero
				duration
	cue in	0.0 / 57.4	0.0 / 57.4 0.2 / 4.3
	cue out	163.9 / 418.0 166.6 / 428.8 2.0 / 14.6
	track end 182.0 / 741.1 182.0 / 741.1 n/a

Table 4 .

 4 Feedback on individual tracks in the first version of cue-point detection output by human expert, and assessment of computability.

	position evaluator remark	computable observations
	end	end applause should be faded out no	music continues during applause
	end	just noises?	no	free guitar + voice
	end	too noisy and weird	no	fade in of 2sec of Morse code
	start	intro too long?	yes	intro very low volume -24dB
	end	outro too long	yes	outro -15 dB (last struct segment silence)

http://abcdj.eu

http://heardis.com Copyright: c 2018 Diemo Schwarz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution

3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

http://www.mixotic.net

http://audacity.sourceforge.net

These times are suggested by the ground truth database.

Note that our algorithm will only ever encounter professionally produced music that is optimised for being loud and punchy to stand out in radio or streaming listening conditions, so we're fairly confident that that threshold will be generalisable.

Acknowledgments

The ABC DJ project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 688122.


Abdou Simon Senghor

An autoethnography of a patient with kidney renal

failure -How experiences and social ties promote therapeutic mobility

Introduction

Health choices are perceived as constructed and resulting from processes [START_REF] Guienne V | Nos choix de santé[END_REF] . These health choices (such as decisions) are shape through the trajectories of patients 2 . The latter are placed in social or medical relations which influence the nature of their care. Patients are involved in a logic of participation in health decisions whose autonomous choice is built through a dependence on various actors 3 .

Health democracy in France along the law of March 4, 2002 and the promotion of shared decision-making in Anglo-Saxon countries by some researchers 4 both advocate an increased participation of patients in decisions by reducing medical paternalism which subjected them to the doctors' authority 5 . An interprofessional form of shared decision making 6 as well as research undertaken in Africa, have demonstrated the major part played by the family and relatives in both decisions and care 7,8 .

Besides, the health status of the patients and the quality of care are related to the performance of the local healthcare system and to the economic problems faced by Coll. Antropol. 41 (2017) 3: ???-???

Original scientific paper

An Autoethnography of a Patient with Kidney Renal Failure -How Experiences and Social Ties Promote Therapeutic Mobility

Abdou Simon Senghor

The Center for Social Anthropology, University Toulouse Jean Jaurès, Toulouse, France

A B S T R A C T

This paper deals with the determinants that encourage therapeutic mobility from Senegal to France. I am both a patient and a researcher and my thesis focused on how health professionals make decisions to refer patients to a dialysis method. I also underlined how patients with kidney failure construct their choice of dialysis method. As a patient in Senegal, a low income country, I experienced some difficulties due to insufficient human resources in the health sector, the little number of dialysis centers, medical pluralism and low performance of the health system.. Choosing a dialysis method depended on many factors such as doctors' opinions, my family's approach and my student status at that time, among other things. That situation resulted in the involvement of health professionals, of my family, who both talked me into going to France in order to get a kidney transplant. Therefore, I will analyse therapeutic mobility as the outcome of social factors and personal experience beyond social bifurcations. Then, I propose here, from my experience, how to move from low performance health system (Senegal) to a high performance one (France) through an autoethnography analysis.

Keywords: autoethnography, reflexivity, social bifurcation, therapeutic mobility, chronic kidney disease.

the populations who are being treated. In some African countries, there are economic determinants that do not allow patients to benefit from good care 9 . In addition, socio-cultural contexts define how patients are cared for. These shortcomings and the search for better quality of care cause some patients to emigrate 10 . This article deals with my care path analyzed through social bifurcations and autoethnography. This approach will make it possible to understand the reasons for my therapeutic mobility in France in order to benefit from a renal transplantation. It outlines the determinants of choice of a dialysis method (hemodialysis, peritoneal dialysis) and finally the choice of renal transplantation concretized by my emigration to France.

Material and methods

Autoethnography

In the Anglo-Saxon literature, the term "autoethnography" is used by a number of researchers [11][12][13] . It is also known as "autobiographical sociology" 14 . For the latter, Received for publication August, 30 2017 self-analysis makes it possible to highlight what makes the person both similar and different from others. According to Ellis and Bochner 15 , autoethnography is: "...an autobiographical genre of writing that displays multiple layers of consciousness, connecting the personal to the cultural". Having a narrative approach with a reflexive attitude does not mean writing an autobiography 16,17 because I narrate events so as to provide a sociological explanation for my health choices. Confronting the social phenomena related to the theories and those defended mainly by sociologists and anthropologists requires some distance from my object of study as well as from myself. From the sociological point of view and in consideration of the research that I carry out on decision-making, this autoethnography focuses on the way in which the health professionals (doctors, nurses …), my relatives and I were involved in decisions regarding the choice of a dialysis method. Consequently, I will analyse the weight of sociocultural and economic realities in the construction of patient choices and the decisions of doctors in Senegal. My aim is also to highlight the role of social networks as well as the strategies implemented to make effective choices such as going to France in order to have the opportunity to benefit from a renal transplant.

However, this autoethnography approach has raised criticisms about its scientific validity, as Walford 18 who perceives autoethnography as a fiction. Other criticisms concern, among other things, the fact that self-centeredness is too centered on the self of the narrator, which can lead the narrator to be self-indulgent, narcissistic or introspective and individualistic 13,19,20 . One limitation I would like to outline is that I am not able to remember some past events as Strasser 21 noted when discussing the limits of autobiographical writing. This limitation may be noticed in my autoethnography. That is why, before the beginning of the narrative, I'd rather acknowledge my inability to remember precisely those previous situations that marked my care path. In order to overcome this shortcoming, I have decided to collect other people's viewpoint (doctors, parents...) to reconstruct their roles in the decision-making process.

Sociologists talking about themselves live in particular social situations, which situations can also be similar to those experienced by other people. This perception of the autoethnography different from the autobiography has led me to adopt a type of writing that will allow me, as the anthropologist Murphy 16 who was paralyzed did, to trace how the disease has upset my social relations by using a socio-anthropological analysis of my care path.

Bifurcations as analysis model

Understanding the way the events led me (or my parents) to choose a replacement therapy for chronic kidney disease or to emigrate to France, brought me to use the bifurcation theory similar to the notion of turning point defined by Abbott 22 as being " more consequential than trajectories because they give rise to changes in overall direction or regime, and do so in determining fashion". The work on the bifurcations carried out by French soci-ologists [23][24][25][26] emphasize the unpredictability of events contrary to turning points.

In my care path, I experienced three replacement treatments for chronic kidney disease: the hemodialysis, peritoneal dialysis and renal transplantation. Transitions from one treatment to another were favoured by medical decisions and some choices my parents made for me. The experience of the disease and its treatment methods can be analysed as the occurrence of uncontrolled events underpinned by sudden changes, as a biographical disruption 27 , or as the unpredictability and irreversibility of events [23][24][25] . In addition, individual representations of illness vary according the social groups they belong to which may define the therapeutic itineraries they take 28 The autoethnographic approach will focus on identifying bifurcation points, paying particular attention to their consequences in terms of my social relations and the decisions they have helped me to make. In addition to the unpredictable and irreversible nature of their foundations, social bifurcations are fuelled by "ingredients" 23 . Indeed, I relied on those ingredients to make some changes both in my care path and in my life as a whole.

Results and discussion -Bifurcations and their relational and decisional consequences

The first bifurcation was the beginning of the dialysis. This first dialysis session caused a biographical disruption 27 in my life. While I was 22 years old and at university, I was diagnosed with kidney failure. I continued my studies and carried on sharing the activities of my university. This first dialysis session therefore put an end to my studies. My university was five hundred kilometres from the capital, where the only public dialysis center was located at that time. In addition to this medical diagnosis and treatment, I was taken to the traditional healer by my parents. This diagnosis suggested that my kidney failure resulted from a spell cast by a college fellow. A treatment was suggested by the traditional healer who gave me amulets to wear and potions to cure this renal insufficiency which he considered to be of supernatural cause. This medical pluralism in the treatment of my kidney failure reflected my parents' approach who thought that a mix of traditional and modern medicine would increase my chances of recovery 29 .

One of the consequences of the hemodialysis was a change in the position I held in my family and resulted in some kind infantilization. Murphy 16 had also experienced such a change with his wife who acted as a mother to her child.

A few months after my hemodialysis, doctors suggested a peritoneal dialysis. This choice for a peritoneal dialysis was apparently due to a lack of space in the hemodialysis center. The other reason was that I was a student and peritoneal dialysis would allow me to go back to university. My parents were reluctant, but they finally agreed to let me go back to university without providing amulets to protect me. Besides the medical equipment provided by the dialysis center in Dakar, my parents had made me produce the amulets as a supernatural "material". Such seemed to be the condition for my return to university. Then, the choice of peritoneal dialysis seemed to be designed on the influence of multiple people as Rapley 30 emphasized it in his study when he considers distributed the decisions.

Besides, this new change was akin to what Hélardot 24 considers as a resistance to the disease. Indeed, I resumed my studies. With the treatment of peritoneal dialysis that I did at night, I was more autonomous during the day. Renal insufficiency no longer prevented me from carrying out certain activities and I was able to defend my master's thesis in sociology of work and organizations at the Gaston Berger University in Saint-Louis in 2008.

The second bifurcation point occurred at that time. One week after my defense, I contracted a peritonitis which forced me to return to Dakar to be treated in the nephrology department. I experienced a series of painful episodes: first of all, the catheter, which was placed in the abdomen for the purpose of peritoneal dialysis, had to be removed in order to treat peritonitis. I had not the possibility to make a choice for another replacement therapy because I was in an emergency situation. Indeed, as Kaufman, Shim and Russ 31 noticed it "In some of those cases, the patient's nearemergency condition and subsequent diagnosis precluded patient choice entirely". I was treated again by hemodialysis, which led to some complications. I had a vascular access problem, the arteriovenous fistulas did not work and the catheters placed alternately at the level of the groin and at the level of the jugulars eventually caused thromboses. I finally ended up with acute oedema of the lungs because I had spent a fortnight without dialysis. A solution was subsequently found by placing a catheter with the assistance of a computer.

In economic terms, these complications impoverished my parents because of the high cost of medicines to buy and the medical analyses to be undertaken. Although my university was supposed to cover the cost of my dialysis sessions by my university, some sessions were not paid for, so my state of health was getting worse. Diallo 9 refers to this reality when he points out that poor people who do not have adequate financial resources are not taken care of in health facilities.

These stressful situations experienced during my care in Senegal are the ingredients that influenced me in my choice to emigrate to France. Two of my sisters who were already in Europe began to look for a way to make me leave Senegal. One of them who worked as an au pair for a Belgian doctor told him about my illness and both started to find ways to raise the money required for a kidney transplant in Belgium.. For my part, I started the preregistration process via the Campus France platform. The outcome was positive and I was able to join France not only to study but also to undergo treatment as I was also registered in the option of therapeutic mobility 10 . At the same time, both my care and studies helped me diversify my goals, which change my approach from a form of a temporary stay or a primary objective of medical care. Indeed, I neither felt I was experiencing some kind of exile nor of medical tourism 32 .

The decision to emigrate was therefore based on experience but also on the mobilization of a social network capable of promoting positive changes, a bifurcation, which was the opportunity to benefit from a renal transplantation whose outcome was both irreversible and unpredictable in terms of future experiences and choices.

Conclusion

Applying some reflexive analysis to my situation of a patient in interaction with his social environment proved necessary to distance myself from my own experience; otherwise one could fall into the trap of narcissism as noted by some researchers. The dual posture of researcher and patient has thus contributed to enrich the methods of analysis used in social sciences. Initiating the process of the autoethnography in the understanding of the trajectory of a patient with renal failure, without falling into easy generalization, has enabled me to grasp individual singularity in a given sociocultural context and diversifies the approach to chronic patients. In addition, this particular narrative process focusing on health choices can be used to explain and develop the different factors that may drive individuals to mobility. The reasons for my therapeutic mobility finally emerged from experiencing painful medical treatments and financial difficulties.

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A. Simon Senghor

The Center for Social Anthropology, University Toulouse Jean Jaurès, 5 allées Antonio Machado, 31058 Toulouse Cedex 9, France e-mail: bensimon.senghor@laposte.net

AUTOETNOGRAFIJA BOLESNIKA SA ZATAJENJEM BUBREGA -KAKO ISKUSTVO I SOCIJALNE VEZE PROMIČU TERAPEUTSKU MOBILNOST S A Ž E T A K

Ovaj se rad bavi odrednicama koje potiču terapeutsku mobilnost iz Senegala u Francusku. Ja sam i pacijent i istraživač, a moja je teza usmjerena na to kako zdravstveni stručnjaci donose odluke o upućivanju pacijenata na metodu dijalize. Također sam naglasio kako pacijenti s zatajenjem bubrega konstruiraju svoj izbor metode dijalize. Kao bolesnik u Senegalu, zemlji s niskim prihodima, doživio sam neke poteškoće zbog nedostatka ljudskih resursa u zdravstvenom sektoru, malog broja centara za dijalizu, medicinskog pluralizma i slabih performansi zdravstvenog sustava. Odabir metode dijalize ovisi, između ostalog, o mnogim čimbenicima kao što su liječnička mišljenja, pristup moje obitelji i mog studentskoh statusa u to vrijeme. Ta je situacija rezultirala uključivanjem zdravstvenih djelatnika, moje obitelji, koji su me oboje nagovarali na odlazak u Francusku kako bi dobili transplantaciju bubrega. Stoga ću analizirati terapijsku mobilnost kao ishod socijalnih čimbenika i osobnog iskustva izvan društvenih bifurkacija. Zatim predlažem ovdje, iz mog iskustva, kako se preseliti iz zdravstvenog sustava niske učinkovitosti (Senegal) na visoko učinkovitu (Francuska) kroz autoetnografsku analizu.

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