Section: New Results
A general framework for audio source separation
Participants : Alexis Benichoux, Frédéric Bimbot, Charles Blandin, Ngoc Duong, Rémi Gribonval, Nobutaka Ito, Alexey Ozerov, Emmanuel Vincent.
Main collaborations: H. Tachibana (University of Tokyo, JP), N. Ono (National Institute of Informatics, JP)
Source separation is the task of retrieving the source signals underlying a multichannel mixture signal. The state-of-the-art approach, which we presented in a survey chapter  , consists of representing the signals in the time-frequency domain and estimating the source coefficients by sparse decomposition in that basis. This approach relies on spatial cues, which are often not sufficient to discriminate the sources unambiguously. Last year, we proposed a general probabilistic framework for the joint exploitation of spatial and spectral cues  that was disseminated in several invited talks  ,  . This framework relies in particular on the thesis of Ngoc Duong, which was defended this year  . It makes it possible to quickly design a new model adapted to the data at hand and estimate its parameters via the EM algorithm. As such, it is expected to become the basis for a number of works in the field, including our own.
Since the EM algorithm is sensitive to initialization, we devoted a major part of our work to reducing this sensitivity. One approach is to set probabilistic priors over the model parameters, including spatial position priors  or temporal continuity priors  . A complementary approach is to initialize the parameters in a suitable way using source localization techniques specifically designed for environments involving multiple sources and possibly background noise  ,  ,  . In a longer-term perspective, we also investigated the design and exploitation of sparsity priors over time-domain acoustic transfer functions  ,  .
Exploiting filter sparsity for source localization and/or separation
Participants : Alexis Benichoux, Prasad Sudhakar, Emmanuel Vincent, Rémi Gribonval, Frédéric Bimbot.
Main collaboration: Simon Arberet (EPFL)
Estimating the filters associated to room impulse responses between a source and a microphone is a recurrent problem with applications such as source separation, localization and remixing.
We considered the estimation of multiple room impulse responses from the simultaneous recording of several known sources. Existing techniques were restricted to the case where the number of sources is at most equal to the number of sensors. We relaxed this assumption in the case where the sources are known. To this aim, we proposed statistical models of the filters associated with convex log-likelihoods, and we proposed a convex optimization algorithm to solve the inverse problem with the resulting penalties. We provided a comparison between penalties via a set of experiments which shows that our method allows to speed up the recording process with a controlled quality tradeoff. This work was presented at two conferences  ,  and a journal paper including extensive experiments with real data is in preparation.
We also investigated the filter estimation problem in a blind setting, where the source signals are unknown. We proposed an approach for the estimation of sparse filters from a convolutive mixture of sources, exploiting the time-domain sparsity of the mixing filters and the sparsity of the sources in the time-frequency (TF) domain. The proposed approach is based on a wideband formulation of the cross-relation (CR) in the TF domain and on a framework including two steps: (a) a clustering step, to determine the TF points where the CR is valid; (b) a filter estimation step, to recover the set of filters associated with each source. We proposed for the first time a method to blindly perform the clustering step (a) and we showed that the proposed approach based on the wideband CR outperforms the narrowband approach and the GCC-PHAT approach by between 5 dB and 20 dB. This work has been published at ICASSP 2011  and submitted for publication as a journal paper.
On a more theoretical side, we studied the frequency permutation ambiguity traditionnally incurred by blind convolutive source separation methods. We focussed on the filter permutation problem in the absence of scaling, investigating the possible use of the temporal sparsity of the filters as a property enabling permutation correction. The obtained theoretical and experimental results highlight the potential as well as the limits of sparsity as an hypothesis to obtain a well-posed permutation problem. This work has been submitted for publicatoin as a journal paper 
Towards real-world separation and remixing applications
Participants : Valentin Emiya, Alexey Ozerov, Laurent Simon, Emmanuel Vincent.
Shoko Araki (NTT Communication Science Laboratories, JP), Cédric Févotte (Télécom ParisTech, FR), Antoine Liutkus (Télécom ParisTech, FR), Volker Hohmann (University of Oldenburg, DE)
Following our founding role in the organization of a regular source separation evaluation campaign (SiSEC), we wrote an invited paper summarizing the outcomes of the three latest campaigns  . While some challenges remain, this paper highlighted that progress has been made and that audio source separation is closer than ever to successful industrial applications. This is also exemplified by the i3DMusic project and the contract recently signed with MAIA Studio.
In order to exploit our know-how for these real-world applications, we investigated issues such as how to implement our algorithms in real time and how best to exploit human input or metadata  ,  . In addition, while the state-of-the-art quality metrics previously developed by METISS remain widely used in the community, we proposed a new set of perceptually motivated metrics which greatly increase correlation with subjective assessments  .
Source separation for multisource content indexing
Participants : Kamil Adiloglu, Alexey Ozerov, Emmanuel Vincent.
Main collaborations: J. Barker (University of Sheffield, UK), M. Lagrange (IRCAM, FR)
Another promising real-world application of source separation concerns information retrieval from multisource data. Source separation may then be used as a pre-processing stage, such that the characteristics of each source can be separately estimated. The main difficulty is not to amplify errors from the source separation stage through subsequent feature extraction and classification stages. To this aim, we proposed a principled Bayesian approach to the estimation of the uncertainty about the separated source signals  and propagated this uncertainty to the features. We then exploited it in the training of the classifier itself, thereby greatly increasing classification accuracy  .
While our work in this direction was initially motivated by music applications (e.g. artist recognition by separating the vocals from the accompaniment), we eventually applied it to noise-robust speech recognition, which is a better defined task  . In order to motivate further work byt the community, we created a new international evaluation campaign on that topic (CHiME)  .