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<!-- <!DOCTYPE html> -->
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Tailleur</a><span class="black-text">PhD Student</span>
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<a aria-label="Navigate to the Publications section" href="#publications"
class="waves-effect waves-dark teal-text"><i
class="mdi-av-my-library-books small"></i><span>Publications</span></a>
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<!-- Second Section: About -->
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<h3 class="page-title white-text teal">About</h3>
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<p>
I am a postdoctoral researcher specializing in the sound field with a focus on deep learning.
Through my expertise in research and my experiences as a sound engineer, I aim to contribute meaningfully to the advances of the audio research field.
Here, you'll find details about my academic journey, skills, and projects. Thank you for taking the time to visit !
</p>
</div>
</section>
<!-- Seventh Section: Education -->
<section id="education" class="section scrollspy">
<h3 class="page-title white-text teal">Education</h3>
<div class="container">
<div class="row">
<!-- École Centrale de Nantes -->
<div class="col s12 m6 l6">
<div class="card">
<div class="card-content">
<p>
<span class="card-title">
<a href="https://www.ec-nantes.fr/" target="_blank" class="teal-text hoverline">
École Centrale de Nantes
</a>
</span>
</p>
<p class="brown-text">Nantes, France</p>
<p><b>Degree:</b> PhD in Computer Science (ongoing) </p>
<ul>
<p><b>Relevant Courseworks:</b></p>
<ul>
<li>Deep Learning</li>
<li>Acoustics</li>
<li>Cartography</li>
</ul>
</ul>
</div>
</div>
</div>
<!-- École Nationale Supérieure Louis-Lumière -->
<div class="col s12 m6 l6">
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<p>
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<a href="https://www.ens-louis-lumiere.fr/" target="_blank" class="teal-text hoverline">
École Nationale Supérieure Louis-Lumière
</a>
</span>
</p>
<p class="brown-text">Saint-Denis, France</p>
<p><b>Degree:</b> Master in Sound</p>
<ul>
<p><b>Relevant Courseworks:</b></p>
<ul>
<li>Sound Recording</li>
<li>Acoustics and Psychoacoustics</li>
<li>Film Sound Design</li>
<li>Audio Post-Production</li>
</ul>
</ul>
</div>
</div>
</div>
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<div class="card">
<div class="card-content">
<p>
<span class="card-title">
<a href="https://www.artsetmetiers.fr/eng" target="_blank" class="teal-text hoverline">
Arts et Métiers
</a>
</span>
</p>
<p class="brown-text">Châlons-en-Champagne, France</p>
<p><b>Degree:</b> Engineer Diploma </p>
<ul>
<p><b>Relevant Courseworks:</b></p>
<ul>
<li>Mechanics</li>
<li>Database Management Systems</li>
<li>Operating Systems</li>
<li>Electronics</li>
</ul>
</ul>
</div>
</div>
</div>
<!-- Politecnico Di Bari -->
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<div class="card">
<div class="card-content">
<p>
<span class="card-title">
<a href="http://www.poliba.it/" target="_blank" class="teal-text hoverline">
Politecnico Di Bari
</a>
</span>
</p>
<p class="brown-text">Bari, Italy</p>
<p><b>Degree:</b> Master in Mechanical Engineering</p>
<ul>
<p><b>Relevant Courseworks:</b></p>
<ul>
<li>Mechanics Applied to Aeronautics</li>
<li>Management of Innovation</li>
<li>Labor Law</li>
</ul>
</ul>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Third Section: Experience -->
<section id="experience" class="section scrollspy">
<h3 class="page-title white-text teal">Experience</h3>
<div class="container">
<!-- Latest Experience -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="http://www.ls2n.fr/equipe/sims/?lang=en" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-ls2n.jpeg" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m10">
<p>
<span class="card-title"><a href="http://www.ls2n.fr/equipe/sims/?lang=en" target="_blank"
class="teal-text hoverline">Postdoctoral Researcher <br> - LS2N - SIMS Team</a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">Postdoctoral Researcher</div>
<ul>
<p>
Perceptual enhancement of sound environments and speech using audio concealing techniques
<br>👉 Deep learning: generative models, TTS (Text-to-Speech) and ASR (Automatic Speech Recognition) models
<br>👉 Statistical analysis, experimental design, auditory perception, cognitive psychology
<br>👉 Acoustic scene analysis, audio signal processing, programming (Python)
</p>
<p>
<b>Skills:</b> Computer Science, Statistics, Python, Deep Learning, Acoustics, Cartography
</p>
</ul>
</div>
<div class="card-action">
<span>Oct 2025 - June 2027 | Nantes, France</span>
</div>
</div>
<!-- Latest Experience -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="http://www.ls2n.fr/equipe/sims/?lang=en" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-ls2n.jpeg" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m10">
<p>
<span class="card-title"><a href="http://www.ls2n.fr/equipe/sims/?lang=en" target="_blank"
class="teal-text hoverline">PhD Student - LS2N - SIMS Team</a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">PhD Student</div>
<ul>
<p>
Sound source detection for the sensitive mapping of urban sound environments.
<br>👉 Deep Learning: classification, generative models
<br>👉 Statistical analysis, experimental design, auditory perception, cognitive psychology
<br>👉 Acoustic scene analysis, audio signal processing, programming (Python)
</p>
<p>
<b>Skills:</b> Computer Science, Statistics, Python, Deep Learning, Acoustics, Cartography
</p>
</ul>
</div>
<div class="card-action">
<span>Sept 2022 - Sept 2025 | Nantes, France</span>
</div>
</div>
<!-- Latest Experience -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://www.ec-nantes.fr/" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-centrale.jpg" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m10">
<p>
<span class="card-title"><a href="http://www.ls2n.fr/equipe/sims/?lang=en" target="_blank"
class="teal-text hoverline">Teaching - ECN / ENSLL</a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">PhD Student</div>
<ul>
<p>
Teaching students from the 1st to the 3rd cycle at Ecole Centrale de Nantes (ECN) (approximately 100h). Supervision of
a master's student internship, of 2 masters students' thesis at Ecole Nationale Supérieure Louis-Lumière (ENSLL),
and of 4 master's students projects.
<br>👉 Deep Learning
<br>👉 algorithms and programming
<br>👉 signal processing
<br>👉 SQL database
</p>
<p>
<b>Skills:</b> Pedagogy, Python, C++, SQL, Deep Learning, Acoustics
</p>
</ul>
</div>
<div class="card-action">
<span>Sept 2022 - Sept 2025 | Nantes, France</span>
</div>
</div>
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a target="_blank"><img alt="Meditab logo"
src="/assets/img/mt-sound.jpg" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m10">
<p>
<span class="card-title"><a target="_blank"
class="teal-text hoverline">Freelance Sound Engineering</a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">Sound Editor, Sound Mixer</div>
<ul>
<p>
Freelance sound engineer working for radio, music and cinema
<br>👉 Sound mixing and sound editing for the cinema industry, for music, and for sound fictions
<br>👉 Direction of a radio program for Prun' radio
</p>
<b>Skills:</b> ProTools, Reaper
</li>
</ul>
</div>
<div class="card-action">
<span>Sep 2018 - July 2022 | France</span>
</div>
</div>
</section>
<!-- Fifth Section: Community -->
<section id="projects" class="section scrollspy">
<h3 class="page-title white-text teal">Projects</h3>
<div class="container">
<!-- Visualization Contribution -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://github.com/mathieulagrange/iasisGdrVisualization?tab=readme-ov-file" target="_blank"><img alt="Visualization" src="/assets/img/mt-iasis.png" class="responsive-img center-block" /></a>
</div>
<div class="col s12 m10">
<span class="card-title"><a href="https://github.com/mathieulagrange/iasisGdrVisualization?tab=readme-ov-file" target="_blank" class="teal-text hoverline">Skills and Research Areas Map for GDR IASIS</a></span>
<p>
I created a dynamic visualization that maps the various research areas within the IASIS research group.
The visualization uses a circle-packing layout: the more people involved in a research area, the larger
its corresponding bubble. Two complementary views are available. In the first, the top-level bubbles represent
regions of France, followed by laboratories or companies, and finally the research areas. In the second view,
the hierarchy is reversed: it starts with research areas, then shows the affiliated labs or companies.
This interactive tool allows users to explore the network's structure and identify key areas of
expertise accross the institutions involved.
</p>
</div>
</div>
</div>
<div class="card-action">
<span>2024</span>
</div>
</div>
<!-- DCASE Challenge Organization -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://dcase.community/challenge2024/task-sound-scene-synthesis" target="_blank"><img alt="DCASE 2024 Task 7" src="/assets/img/mt-dcase-task7.png" class="responsive-img center-block" /></a>
</div>
<div class="col s12 m10">
<span class="card-title"><a href="https://dcase.community/challenge2024/task-sound-scene-synthesis" target="_blank" class="teal-text hoverline">Organization of DCASE 2024 Task 7 – Sound Scene Synthesis</a></span>
<p>
Organizer and dataset creator for DCASE Task 7: Sound Scene Synthesis, part of the DCASE 2024 Challenge. I led the dataset design and collection,
defined the task protocol, and helped in coordinating the evaluation setup. The task focuses on generating realistic urban sound scenes from
textual descriptions. It aims to support the development of generative models for urban soundscapes.
</p>
</div>
</div>
</div>
<div class="card-action">
<span>2024</span>
</div>
</div>
</div>
</section>
<!-- Fourth Section: Datasets -->
<section id="datasets" class="section scrollspy">
<h3 class="page-title white-text teal">Datasets</h3>
<div class="container">
<div class="row">
<div class="col s12 m6 l4">
<div class="card medium">
<div class="card-image waves-effect waves-block waves-light">
<img alt="music streaming app" src="/assets/img/mt-emvd-reformated.png" style="height: 100%; width: 100%" class="activator" />
</div>
<div class="card-content">
<span class="card-title activator teal-text hoverline">Extreme Metal Vocals Dataset<i
class="mdi-navigation-more-vert right"></i></span>
<p>
Extreme Metal Vocals Dataset (EMVD) is a dataset of extreme vocal distortion techniques used in heavy metal
</p>
</div>
<div class="card-reveal">
<!-- TODO: change this -- only close button -->
<!-- <span class="card-title brown-text">Accomplishments<i class="mdi-navigation-close right"></i></span> -->
<span class="card-title grey-text"><small>Accomplishments</small><i
class="mdi-navigation-close right"></i></span>
<ul>
<p>The dataset consists of 760 audio
excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly composed
of 60 minutes of distorted voices and 40 minutes of clear voice recordings. These vocal recordings
are from 27 different singers and are provided without accompanying musical instruments or
post-processing effects.
</p>
</ul>
<div class="card-action">
<a aria-label="Visit " href="https://zenodo.org/records/8406322" target="_blank" data-position="top"
data-tooltip="View Online"
class="btn-floating btn-large waves-effect waves-light blue-grey tooltipped"><i
class="fa fa-external-link"></i></a>
<!-- <a aria-label="Visit the GitHub repo for project" href="https://github.com/varadbhogayata/music-player"
target="_blank" data-position="top" data-tooltip="View Source"
class="btn-floating btn-large waves-effect waves-light blue-grey tooltipped"><i
class="fa fa-github"></i></a> -->
</div>
</div>
</div>
</div>
<div class="col s12 m6 l4">
<div class="card medium">
<div class="card-image waves-effect waves-block waves-light">
<img alt="music streaming app" src="/assets/img/mt-dcase-task7-reformated.png" style="height: 100%; width: 100%" class="activator" />
</div>
<div class="card-content">
<span class="card-title activator teal-text hoverline">DCASE 2024 Task 7 Dataset - OS<i
class="mdi-navigation-more-vert right"></i></span>
<p>
This dataset supports the development and evaluation of generative algorithms for environmental
sound synthesis.
</p>
</div>
<div class="card-reveal">
<!-- TODO: change this -- only close button -->
<!-- <span class="card-title brown-text">Accomplishments<i class="mdi-navigation-close right"></i></span> -->
<span class="card-title grey-text"><small>Accomplishments</small><i
class="mdi-navigation-close right"></i></span>
<ul>
<p>The DCASE 2024 Task 7 Dataset - Open Source dataset includes 310 audio clips, each 4 seconds long,
along with their corresponding text prompts. Unlike typical
audio captioning datasets, both the prompts and audio scenes
were manually crafted and edited. This enables a more controlled
and quantifiable evaluation of generative models.
</p>
</ul>
<div class="card-action">
<a aria-label="Visit " href="https://zenodo.org/records/15630417" target="_blank" data-position="top"
data-tooltip="View Online"
class="btn-floating btn-large waves-effect waves-light blue-grey tooltipped"><i
class="fa fa-external-link"></i></a>
<!-- <a aria-label="Visit the GitHub repo for project" href="https://github.com/varadbhogayata/music-player"
target="_blank" data-position="top" data-tooltip="View Source"
class="btn-floating btn-large waves-effect waves-light blue-grey tooltipped"><i
class="fa fa-github"></i></a> -->
</div>
</div>
</div>
</div>
<div class="col s12 m6 l4">
<div class="card medium">
<div class="card-image waves-effect waves-block waves-light">
<img alt="quiz app" src="/assets/img/mt-cityspeechmix-reformated.png" style="height: 100%; width: 100%" class="activator" />
</div>
<div class="card-content">
<span class="card-title activator teal-text hoverline">CitySpeechMix<i
class="mdi-navigation-more-vert right"></i></span>
<p>
CitySpeechMix is a simulated dataset of speech and urban sound mixtures from LibriSpeech and SONYC-UST
</p>
</div>
<div class="card-reveal">
<!-- TODO: change this -- only close button -->
<!-- <span class="card-title brown-text">Accomplishments<i class="mdi-navigation-close right"></i></span> -->
<span class="card-title grey-text"><small>Accomplishments</small><i
class="mdi-navigation-close right"></i></span>
<ul>
<p>
The dataset consists of 742 audio clips , each 10 seconds long:
<ul>
<li>371 mixtures of speech over urban background noise</li>
<li>371 voice-free urban environmental recordings</li>
</ul>
</p>
</ul>
<div class="card-action">
<a aria-label="Visit " href="https://zenodo.org/records/15405950" target="_blank" data-position="top"
data-tooltip="View Online"
class="btn-floating btn-large waves-effect waves-light blue-grey tooltipped"><i
class="fa fa-external-link"></i></a>
<!-- <a aria-label="Visit the GitHub repo for project" href="https://github.com/varadbhogayata/QuizUp"
target="_blank" data-position="top" data-tooltip="View Source"
class="btn-floating btn-large waves-effect waves-light blue-grey tooltipped"><i
class="fa fa-github"></i></a> -->
</div>
</div>
</div>
</div>
<!-- ************* -->
</div>
</div>
</section>
<!-- Sixth Section: Publications -->
<section id="publications" class="section scrollspy">
<h3 class="page-title white-text teal">Publications</h3>
<div class="container">
<!-- Journals Subsection -->
<h4 class="teal-text">Journals</h4>
<div class="row">
<!-- JASA Journal Card -->
<div class="col s12">
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://pubs.aip.org/asa/jasa/pages/cfp030223" target="_blank">
<img alt="Zhiffy logo" src="/assets/img/mt-jasa.png" class="responsive-img center-block" />
</a>
</div>
<div class="col s12 m8">
<p>
<span class="card-title">
<a href="https://hal.science/hal-04676606" target="_blank" class="teal-text hoverline">
Sound source classification for soundscape analysis using fast third-octave bands data from an urban acoustic sensor network
</a>
</span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">JASA</div>
<p class="authors grey-text text-darken-1" style="margin-bottom: 1.5em;">
<strong>Authors:</strong> Modan Tailleur, Pierre Aumond, Mathieu Lagrange, Vincent Tourre
</p>
<p>
The exploration of the soundscape relies strongly on the characterization of the sound sources in the sound environment.
Novel sound source classifiers, called pre-trained audio neural networks (PANNs), are capable of predicting the presence
of more than 500 diverse sound sources. Nevertheless, PANNs models use fine Mel spectro-temporal representations as input,
whereas sensors of an urban noise monitoring network often record fast third-octaves data, which have significantly lower
spectro-temporal resolution. In a previous study, we developed a transcoder to transform fast third-octaves into the fine
Mel spectro-temporal representation used as input of PANNs. In this paper, we demonstrate that employing PANNs with fast
third-octaves data, processed through this transcoder, does not strongly degrade the classifier's performance in predicting
the perceived time of presence of sound sources. Through a qualitative analysis of a large-scale fast third-octave dataset,
we also illustrate the potential of this tool in opening new perspectives and applications for monitoring the soundscapes of cities.
</p>
</div>
<div class="card-action">
<span>2024</span>
</div>
</div>
<!-- Conferences Subsection -->
<h4 class="teal-text">International Conferences</h4>
<div class="row">
<!-- EUSIPCO 2025 Card -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://eusipco2025.org/" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-eusipco-2025.png" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m8">
<p>
<span class="card-title"><a href="https://hal.science/CEREMA/hal-05096000v1" target="_blank"
class="teal-text hoverline">
Diffusion-based spectral super-resolution of third octave acoustic sensor data: is privacy at risk ?
</a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">EUSIPCO 2025</div>
<p class="authors grey-text text-darken-1" style="margin-bottom: 1.5em;">
<strong>Authors:</strong> Modan Tailleur, Chaymae Benaatia, Mathieu Lagrange, Pierre Aumond, Vincent Tourre
</p>
<p>
Third octave spectral recording of acoustic sensor data is an effective way of measuring the environment.
While there is strong evidence that slow (1s frame, 1 Hz rate) and fast (125ms frame, 8Hz rate) versions
lead by-design to unintelligible speech if reconstructed, the advent of high quality reconstruction methods
based on diffusion may pose a threat, as those approaches can embed a significant amount of a priori knowledge
when learned over extensive speech datasets.
</p>
<p>
This paper aims to assess this risk at three levels of attacks with a growing level of a priori knowledge
considered at the learning of the diffusion model, a) none, b) multi-speaker data excluding the target speaker
and c) target speaker. Without any prior regarding the speech profile of the speaker (levels a and b),
our results suggest a rather low risk as the worderror-rate both for humans and automatic recognition
remains higher than 89%.
</p>
</div>
<div class="card-action">
<span>Sept 2025 | Palermo, Italy</span>
</div>
</div>
<!-- INTERNOISE 2024 Card -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://internoise2024.org/" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-internoise-2024.png" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m8">
<p>
<span class="card-title"><a href="https://hal.science/hal-04593887/" target="_blank"
class="teal-text hoverline">Towards better visualizations of urban sound environments: insights from interviews </a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">INTERNOISE 2024</div>
<p class="authors grey-text text-darken-1" style="margin-bottom: 1.5em;">
<strong>Authors:</strong> Modan Tailleur, Pierre Aumond, Vincent Tourre, Mathieu Lagrange
</p>
<p>
Urban noise maps and noise visualizations traditionally provide macroscopic representations
of noise levels across cities. However, those representations fail at accurately gauging
the sound perception associated with these sound environments, as perception highly depends
on the sound sources involved. This paper aims at analyzing the need for the representations
of sound sources, by identifying the urban stakeholders for whom such representations are assumed
to be of importance. Through spoken interviews with various urban stakeholders, we have gained
insight into current practices, the strengths and weaknesses of existing tools and the relevance
of incorporating sound sources into existing urban sound environment representations.
Three distinct use of sound source representations emerged in this study: 1) noise-related complaints
for industrials and specialized citizens, 2) soundscape quality assessment for citizens, and 3)
guidance for urban planners. Findings also reveal diverse perspectives for the use of visualizations,
which should use indicators adapted to the target audience, and enable data accessibility.
</p>
</div>
<div class="card-action">
<span>Aug 2024 | Nantes, France</span>
</div>
</div>
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://eusipcolyon.sciencesconf.org/" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-eusipco-2024.jpeg" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m8">
<p>
<span class="card-title"><a href="https://arxiv.org/abs/2403.17508/" target="_blank"
class="teal-text hoverline">Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant </a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">EUSIPCO 2024</div>
<p class="authors grey-text text-darken-1" style="margin-bottom: 1.5em;">
<strong>Authors:</strong> Modan Tailleur, Junwon Lee, Mathieu Lagrange, Keunwoo Choi, Laurie M Heller, Keisuke Imoto, Yuki Okamoto
</p>
<p>
This paper explores whether considering alternative domain-specific embeddings to calculate the Fréchet
Audio Distance (FAD) metric can help the FAD to correlate better with perceptual ratings of environmental sounds.
We used embeddings from VGGish, PANNs, MS-CLAP, L-CLAP, and MERT, which are tailored for either music or environmental
sound evaluation. The FAD scores were calculated for sounds from the DCASE 2023 Task 7 dataset. Using perceptual data
from the same task, we find that PANNs-WGM-LogMel produces the best correlation between FAD scores and perceptual ratings
of both audio quality and perceived fit with a Spearman correlation higher than 0.5. We also find that music-specific
embeddings resulted in significantly lower results. Interestingly, VGGish, the embedding used for the original Fréchet
calculation, yielded a correlation below 0.1. These results underscore the critical importance of the choice of embedding
for the FAD metric design.
</p>
</div>
<div class="card-action">
<span>Aug 2024 | Lyon, France</span>
</div>
</div>
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://cbmi2024.org/" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-cbmi-2024.png" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m8">
<p>
<span class="card-title"><a href="https://arxiv.org/abs/2406.17732" target="_blank"
class="teal-text hoverline">EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal</a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">CBMI 2024</div>
<p class="authors grey-text text-darken-1" style="margin-bottom: 1.5em;">
<strong>Authors:</strong> Modan Tailleur, Julien Pinquier, Laurent Millot, Corsin Vogel, Mathieu Lagrange
</p>
<p>
In this paper, we introduce the Extreme Metal Vocals Dataset, which comprises a collection of recordings
of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of
760 audio excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly
composed of 60 minutes of distorted voices and 40 minutes of clear voice recordings. These vocal recordings
are from 27 different singers and are provided without accompanying musical instruments or post-processing effects.
The distortion taxonomy within this dataset encompasses four distinct distortion techniques and three vocal effects,
all performed in different pitch ranges. Performance of a state-of-the-art deep learning model is evaluated for
two different classification tasks related to vocal techniques, demonstrating the potential of this resource
for the audio processing community.
</p>
</div>
<div class="card-action">
<span>Sep 2024 | Reykjavic, Island</span>
</div>
</div>
<!-- Workshops Subsection -->
<h4 class="teal-text">International Workshops</h4>
<div class="row">
<!-- DCASE 2024 Workshop Card - Neonatal -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://dcase.community/workshop2024/index" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-dcase-2024.png" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m8">
<p>
<span class="card-title"><a href="https://hal.science/EC-NANTES/hal-04698561v2" target="_blank"
class="teal-text hoverline">
Machine listening in a neonatal intensive care unit
</a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">DCASE 2024</div>
<p class="authors grey-text text-darken-1" style="margin-bottom: 1.5em;">
<strong>Authors:</strong> Modan Tailleur, Vincent Lostanlen , Mathieu Lagrange, Jean-Philippe Rivière, Pierre Aumond
</p>
<p>
Oxygenators, alarm devices, and footsteps are some of the most common sound sources in a hospital.
Detecting them has scientific value for environmental psychology but comes with challenges of its own:
namely, privacy preservation and limited labeled data. In this paper, we address these two challenges via a
combination of edge computing and cloud computing. For privacy preservation, we have designed an acoustic sensor
which computes third-octave spectrograms on the fly instead of recording audio waveforms. For sample-efficient
machine learning, we have repurposed a pretrained audio neural network (PANN) via spectral transcoding and
label space adaptation. A small-scale study in a neonatological intensive care unit (NICU) confirms that the
time series of detected events align with another modality of measurement: i.e., electronic badges for parents
and healthcare professionals. Hence, this paper demonstrates the feasibility of polyphonic machine listening
in a hospital ward while guaranteeing privacy by design.
</p>
</div>
<div class="card-action">
<span>Sept 2024 | Tokyo, Japan</span>
</div>
</div>
<!-- DCASE 2023 Workshop Card -->
<div class="card">
<div class="card-content">
<div class="row">
<div class="col s12 m2">
<a href="https://dcase.community/workshop2023/index" target="_blank"><img alt="Zhiffy logo"
src="/assets/img/mt-dcase-2023.jpg" class="responsive-img center-block" /></a>
</div>
<!-- <div class="col s12 m10"> -->
<div class="col s12 m8">
<p>
<span class="card-title"><a href="https://hal.science/hal-04178197v2/" target="_blank"
class="teal-text hoverline">Spectral trancoder : using pretrained urban sound
classifiers on undersampled spectral representations</a></span>
</p>
</div>
</div>
<div class="role brown-text text-darken-2">DCASE 2023</div>
<p class="authors grey-text text-darken-1" style="margin-bottom: 1.5em;">
<strong>Authors:</strong> Modan Tailleur, Mathieu Lagrange, Pierre Aumond, Vincent Tourre
</p>
<p>
Slow or fast third-octave bands representations (with a frame
resp. every 1-s and 125-ms) have been a de facto standard for urban
acoustics, used for example in long-term monitoring applications.
It has the advantages of requiring few storage capabilities and of
preserving privacy. As most audio classification algorithms take
Mel spectral representations with very fast time weighting (ex. 10-
ms) as input, very few studies have tackled classification tasks using