An ICA-Based Method for Blind Source Separation in Sparse Domains

Nadalin, Everton Z.; Suyama, Ricardo; Attux, Romis

doi:10.1007/978-3-642-00599-2_75

Cited by 4 publications

(7 citation statements)

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“…In this section, we verify the performance of each aforementioned step when the doping watermarking procedure is employed. For the first two steps, we will use the ICA-SCA based approach proposed in [13,36] that was also used in [22]. In a stereo mixing situation, the algorithm can be summarized as follows:…”

Section: Methodsmentioning

confidence: 99%

“…• The STFT was used for signal representation, and the mixing parameters were estimated using the SCA/ICA approach presented in [13] d , giving GEM algorithm a 'good initialization'. The parameters settings used in FASST correspond to the multichannel NMF method presented in [39] in the instantaneous case.…”

Section: Methodsmentioning

confidence: 99%

“…Another interesting approach is followed in [12] and [13], which combine SCA and ICA methods, proposing that ICA be performed in time-frequency regions in which the number of active sources do not exceed the number of sensors.…”

Section: Introductionmentioning

confidence: 99%

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Perceptually controlled doping for audio source separation

Mahé

Nadalin

Suyama

et al. 2014

EURASIP J. Adv. Signal Process.

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The separation of an underdetermined audio mixture can be performed through sparse component analysis (SCA) that relies however on the strong hypothesis that source signals are sparse in some domain. To overcome this difficulty in the case where the original sources are available before the mixing process, the informed source separation (ISS) embeds in the mixture a watermark, which information can help a further separation. Though powerful, this technique is generally specific to a particular mixing setup and may be compromised by an additional bitrate compression stage. Thus, instead of watermarking, we propose a 'doping' method that makes the time-frequency representation of each source more sparse, while preserving its audio quality. This method is based on an iterative decrease of the distance between the distribution of the signal and a target sparse distribution, under a perceptual constraint. We aim to show that the proposed approach is robust to audio coding and that the use of the sparsified signals improves the source separation, in comparison with the original sources. In this work, the analysis is made only in instantaneous mixtures and focused on voice sources.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Perceptually controlled doping for audio source separation

Mahé

Nadalin

Suyama

et al. 2014

EURASIP J. Adv. Signal Process.

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“…Quando o número de fontes a ser estimado é maior do que o número de sensores, é necessário explorar outras informações a fim de estimar os sinais, como na abordagem da Análise por Componentes Esparsas (Sparse Component Analysis -SCA), que se utiliza da hipótese de que as fontes são esparsas em algum domínio [3].…”

Section: Introductionunclassified

“…Conforme observado em trabalhos anteriores [3][5] a utilização de sinais esparsos leva a uma melhora na estimação do modelo de mistura. Entretanto, ainda é necessário avaliar o impacto do grau de esparsidade das fontes na qualidade da estimação dos sinais.…”

Section: Introductionunclassified

Estimação de Sinais de Voz Esparsificados em Misturas Subparametrizadas

Suzumura¹,

Suyama²

2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-O problema de separação cega de fontes no contexto de misturas subparametrizadas tem sido investigado por meio de abordagens que exploram diferentes características dos sinais de interesse, dentre as quais se destaca a esparsidade. No presente trabalho, comparamos o desempenho de diferentes técnicas de estimação dos sinais de áudio, no contexto de misturas subparametrizadas, considerando que os mesmos são esparsos ou tenham sido esparsificados antes do processo de mistura. Os resultados obtidos estendem análises preliminares realizadas, e indicam que este pré-processamento traz ganhos relativos para o processo de estimação dos sinais.

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Multimodal optimization in the context of Sparse Component Analysis

Nadalin

Boccato

Attux

et al. 2011

2011 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing

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In this work, we investigate the use of a multimodal search framework to deal with a representative formulation of the Sparse Component Analysis (SCA) problem. The proposed method, which employs an artificial immune network in the role of multimodal optimization tool, is explained and tested in different scenarios. The results are promising and indicate the relevance of using global search tool in SCA, as well as the soundness of the immune-inspired proposal.

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An ICA-Based Method for Blind Source Separation in Sparse Domains

Cited by 4 publications

References 10 publications

Perceptually controlled doping for audio source separation

Perceptually controlled doping for audio source separation

Estimação de Sinais de Voz Esparsificados em Misturas Subparametrizadas

Multimodal optimization in the context of Sparse Component Analysis

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