Multi-channel audio source separation using multiple deformed references

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Underdetermined convolutive source separation using two dimensional non-negative factorization techniques

PhD ThesisIn this thesis the underdetermined audio source separation has been considered, that is, estimating the original audio sources from the observed mixture when the number of audio sources is greater than the number of channels. The separation has been carried out using two approaches; the blind audio source separation and the informed audio source separation. The blind audio source separation approach depends on the mixture signal only and it assumes that the separation has been accomplished without any prior information (or as little as possible) about the sources. The informed audio source separation uses the exemplar in addition to the mixture signal to emulate the targeted speech signal to be separated. Both approaches are based on the two dimensional factorization techniques that decompose the signal into two tensors that are convolved in both the temporal and spectral directions. Both approaches are applied on the convolutive mixture and the high-reverberant convolutive mixture which are more realistic than the instantaneous mixture. In this work a novel algorithm based on the nonnegative matrix factor two dimensional deconvolution (NMF2D) with adaptive sparsity has been proposed to separate the audio sources that have been mixed in an underdetermined convolutive mixture. Additionally, a novel Gamma Exponential Process has been proposed for estimating the convolutive parameters and number of components of the NMF2D/ NTF2D, and to initialize the NMF2D parameters. In addition, the effects of different window length have been investigated to determine the best fit model that suit the characteristics of the audio signal. Furthermore, a novel algorithm, namely the fusion K models of full-rank weighted nonnegative tensor factor two dimensional deconvolution (K-wNTF2D) has been proposed. The K-wNTF2D is developed for its ability in modelling both the spectral and temporal changes, and the spatial covariance matrix that addresses the high reverberation problem. Variable sparsity that derived from the Gibbs distribution is optimized under the Itakura-Saito divergence and adapted into the K-wNTF2D model. The tensors of this algorithm have been initialized by a novel initialization method, namely the SVD two-dimensional deconvolution (SVD2D). Finally, two novel informed source separation algorithms, namely, the semi-exemplar based algorithm and the exemplar-based algorithm, have been proposed. These algorithms based on the NMF2D model and the proposed two dimensional nonnegative matrix partial co-factorization (2DNMPCF) model. The idea of incorporating the exemplar is to inform the proposed separation algorithms about the targeted signal to be separated by initializing its parameters and guide the proposed separation algorithms. The adaptive sparsity is derived for both ii of the proposed algorithms. Also, a multistage of the proposed exemplar based algorithm has been proposed in order to further enhance the separation performance. Results have shown that the proposed separation algorithms are very promising, more flexible, and offer an alternative model to the conventional methods

Variational Bayesian Inference for Source Separation and Robust Feature Extraction

International audienceWe consider the task of separating and classifying individual sound sources mixed together. The main challenge is to achieve robust classification despite residual distortion of the separated source signals. A promising paradigm is to estimate the uncertainty about the separated source signals and to propagate it through the subsequent feature extraction and classification stages. We argue that variational Bayesian (VB) inference offers a mathematically rigorous way of deriving uncertainty estimators, which contrasts with state-of-the-art estimators based on heuristics or on maximum likelihood (ML) estimation. We propose a general VB source separation algorithm, which makes it possible to jointly exploit spatial and spectral models of the sources. This algorithm achieves 6% and 5% relative error reduction compared to ML uncertainty estimation on the CHiME noise-robust speaker identification and speech recognition benchmarks, respectively, and it opens the way for more complex VB approximations of uncertainty.Dans cet article, nous considérons le problème de l'extraction des descripteurs de chaque source dans un enregistrement audio multi-sources à l'aide d'un algorithme général de séparation de sources. La difficulté consiste à estimer l'incertitude sur les sources et à la propager aux descripteurs, afin de les estimer de façon robuste en dépit des erreurs de séparation. Les méthodes de l'état de l'art estiment l'incertitude de façon heuristique, tandis que nous proposons d'intégrer sur les paramètres de l'algorithme de séparation de sources. Nous décrivons dans ce but une méthode d'inférence variationnelle bayésienne pour l'estimation de la distribution a posteriori des sources et nous calculons ensuite l'espérance des descripteurs par propagation de l'incertitude selon la méthode d'identification des moments. Nous évaluons la précision des descripteurs en terme d'erreur quadratique moyenne et conduisons des expériences de reconnaissance du locuteur afin d'observer la performance qui en découle pour un problème réel. Dans les deux cas, la méthode proposée donne les meilleurs résultats

Applying source separation to music

International audienceSeparation of existing audio into remixable elements is very useful to repurpose music audio. Applications include upmixing video soundtracks to surround sound (e.g. home theater 5.1 systems), facilitating music transcriptions, allowing better mashups and remixes for disk jockeys, and rebalancing sound levels on multiple instruments or voices recorded simultaneously to a single track. In this chapter, we provide an overview of the algorithms and approaches designed to address the challenges and opportunities in music. Where applicable, we also introduce commonalities and links to source separation for video soundtracks, since many musical scenarios involve video soundtracks (e.g. YouTube recordings of live concerts, movie sound tracks). While space prohibits describing every method in detail, we include detail on representative music‐specific algorithms and approaches not covered in other chapters. The intent is to give the reader a high‐level understanding of the workings of key exemplars of the source separation approaches applied in this domain

Audio source separation using multiple deformed references

International audienceThis paper deals with audio source separation guided by multiple audio references. We present a general framework where additional audio references for one or more sources of a given mixture are available. Each audio reference is another mixture which is supposed to contain at least one source similar to one of the target sources. Deformations between the sources of interest and their references are modeled in a general manner. A nonnegative matrix co-factorization algorithm is used which allows sharing of information between the considered mixtures. We run our algorithm on music plus voice mixtures with music and/or voice references. Applied on movies and TV series data, our algorithm improves the signal-todistortion ratio (SDR) of the sources with the lowest intensity by 9 to 12 decibels with respect to original mixture