Team

Gaël Richard is the scientific co-director of Hi! Paris. 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, environmental 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.

He has received in 2020 the Grand Prix IMT-Académie des Sciences. In 2022 his project Hi-Audio earned him an advanced ERC Grant of the European Union.

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

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Mathieu Fontaine is Associate professor at Télécom Paris, in the S2A team of IDS Department. He's also a member of the ADASP group. After a PhD in Inria Nancy Grand-Est entitled “alpha-stable process for signal processing”, Mathieu Fontaine was a Postdoc from October 2019 to August 2021 at RIKEN 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: Bayesian methods, denoising, signal models, source separation, speech processing

Geoffroy Peeters is Full-professor in the LTCI S2A team at Télécom Paris. He's also a member of the ADASP group. 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 lead research 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 : machine learning, MIR

GitHub

Keywords : deep learning, diffusion models, audio generation and transformation

GitHub

Keywords : deep learning, machine learning, machine listening, speech processing reverberation

GitHub | LinkedIn | Google Scholar

Keywords : signal Processing, deep generative models

LinkedIn

Keywords : explainable audio models, deep learning models, audio signals

GitHub | Google Scholar

Keywords : machine learning, MIR, music generation

GitHub