Equipe académique

Mathieu Fontaine is Associate Professor in the LTCI S2A team at Télécom Paris. After a PhD in Inria Nancy Grand-Est entitled “alpha-stable process for signal processing”, he was a Postdoc from October 2019 to August 2021 at RIKEN Center Artificial Intelligence Project (AIP) and became a guest at Kyoto University. His interests is mainly on machine listening including - but not limited to - speech enhancement, speaker separation, source localization and music source separation using heavy-tailed probabilistic models and/or deep bayesian networks with also applications in augmented reality.

Keywords : Machine listening, Machine Learning, Probability, Augmented Reality

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Geoffroy Peeters is full professor in the LTCI S2A team at Télécom Paris. He received his PHDs degree in 2001 and Habilitation in 2013 from University Paris-VI on audio signal processing, data analysis and machine learning. Before joining Télécom Paris, he led researches related to Music Information Retrieval at IRCAM. His current research work is on signal processing, machine learning and deep learning applied to audio and music data analysis.

Keywords : Audio signal processing, machine learning and deep learning.

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Gaël Richard is Professor at Télécom Paris, Institut Mines-Télécom and head of the Image, Data, Signal (IDS) department. His research work lies at the core of digitization and is dedicated to the analysis, transformation, understanding and automatic indexing of acoustic signals (including speech, music, surrounding sounds) and to a lesser extent of heterogeneous and multimodal signals. In particular, he developed several source separation methods for audio and musical signals based on machine learning approaches.

Keywords : Machine listening, Matrix Factorization, Representation and subspace learning, Music Information Retrieval (MIR), Sound recognition, Audio source separation.

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Roland Badeau is Full Professor in the Signal, Statistics and Machine learning (S2A) team of the Image, Data, Signal (IDS) Department at Télécom Paris. His research interests focus on statistical modeling of non-stationary signals (including adaptive high-resolution spectral analysis and Bayesian extensions to NMF), with applications to audio and music (source separation, denoising, dereverberation, multipitch estimation, automatic music transcription, audio coding, audio inpainting). He is a co-author of 30 journal papers, over 100 international conference papers, and 4 patents. He is also an Associate Editor of the EURASIP Journal on Audio, Speech, and Music Processing and the IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Keywords : machine learning, data decomposition, machine listening, MIR

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