Bayes in Grenoble: Simon Leglaive

on the May 15, 2018

May, 15th at 2:00 pm
Simon Leglaive (Postdoc, Perception, Inria) will talk about "Student’s t Source and Mixing Models for Multichannel Audio Source Separation". The event will take place on May, 15 at 2:00 pm at Inria Monbonnot.
Bayes in Grenoble is a new reading group on Bayesian statistical methods. The purpose of this group is to gather the Grenoble Bayesian community on a monthly basis around noteworthy papers. Those can equally focus on theory, methods, learning, applications, computations, etc, and can be seminal papers as well as recent preprints, as soon as they relate to Bayes.

The sessions last two hours: the presentation is followed by an informal moment where participants will enjoy cocktails and snacks offered by the Grenoble Alpes Data Institute.

The reading group is organised by Julyan Arbel and Florence Forbes. Feel free to contact them if you wish to attend/be added to the mailing list and/or give a talk.

React on social media: #BIGseminar

15 May 2018, Simon Leglaive (Postdoc, Perception, Inria)

Student’s t Source and Mixing Models for Multichannel Audio Source Separation.

This paper presents a Bayesian framework for under-determined audio source separation in multichannel reverberant mixtures. We model the source signals as Student’s t latent random variables in a time-frequency domain. The specific structure of musical signals in this domain is exploited by means of a non-negative matrix factorization model. Conversely, we design the mixing model in the time domain. In addition to leading to an exact representation of the convolutive mixing process, this approach allows us to develop simple probabilistic priors for the mixing filters. Indeed, as those filters correspond to room responses they exhibit a simple characteristic structure in the time domain that can be used to guide their estimation. We also rely on the Student’s t distribution for modeling the impulse response of the mixing filters. From this model, we develop a variational inference algorithm in order to perform source separation. The experimental evaluation demonstrates the potential of this approach for separating multichannel reverberant mixtures.

Paper available at

Published on May 15, 2018

Practical informations


Inria Montbonnot
room F107