Unsupervised spatial dictionary learning for sparse underdetermined multichannel source separation

Nesta, Francesco; Fakhry, Mahmoud

doi:10.1109/icassp.2013.6637614

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…However, the ICA-based separation schemes require large amounts of data recorded in a stationary acoustic condition to provide a reasonable estimate of model parameters. In addition, they impose a permutation problem due to misalignment of the individual source components [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Multiple Speech Source Separation Using Inter-Channel Correlation and Relaxed Sparsity

2018

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In this work, a multiple speech source separation method using inter-channel correlation and relaxed sparsity is proposed. A B-format microphone with four spatially located channels is adopted due to the size of the microphone array to preserve the spatial parameter integrity of the original signal. Specifically, we firstly measure the proportion of overlapped components among multiple sources and find that there exist many overlapped time-frequency (TF) components with increasing source number. Then, considering the relaxed sparsity of speech sources, we propose a dynamic threshold-based separation approach of sparse components where the threshold is determined by the inter-channel correlation among the recording signals. After conducting a statistical analysis of the number of active sources at each TF instant, a form of relaxed sparsity called the half-K assumption is proposed so that the active source number in a certain TF bin does not exceed half the total number of simultaneously occurring sources. By applying the half-K assumption, the non-sparse components are recovered by regarding the extracted sparse components as a guide, combined with vector decomposition and matrix factorization. Eventually, the final TF coefficients of each source are recovered by the synthesis of sparse and non-sparse components. The proposed method has been evaluated using up to six simultaneous speech sources under both anechoic and reverberant conditions. Both objective and subjective evaluations validated that the perceptual quality of the separated speech by the proposed approach outperforms existing blind source separation (BSS) approaches. Besides, it is robust to different speeches whilst confirming all the separated speeches with similar perceptual quality.

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Section: Introductionmentioning

confidence: 99%

Multiple Speech Source Separation Using Inter-Channel Correlation and Relaxed Sparsity

2018

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“…However, they require a large amount of data recorded in a stationary acoustic condition to provide a reasonable estimate of model parameters. In addition, it imposes a permutation problem due to misalignment of the individual source components [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Multiple Speech Source Separation with Non-Sparse Components Recovery by Using Dual Similarity Determination

Jia

Sun

Feng

et al. 2018

IEICE Trans. Inf. & Syst.

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In this work, a multiple source separation method with joint sparse and non-sparse components recovery is proposed by using dual similarity determination. Specifically, a dual similarity coefficient is designed based on normalized cross-correlation and Jaccard coefficients, and its reasonability is validated via a statistical analysis on a quantitative effective measure. Thereafter, by regarding the sparse components as a guide, the non-sparse components are recovered using the dual similarity coefficient. Eventually, a separated signal is obtained by a synthesis of the sparse and non-sparse components. Experimental results demonstrate the separation quality of the proposed method outperforms some existing BSS methods including sparse components separation based methods, independent components analysis based methods and soft threshold based methods. key words: blind source separation, non-sparse component, similarity coefficient, intelligent systems Maoshen Jiareceived the B.

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Independent Vector Analysis Exploiting Pre-learned Banks of Relative Transfer Functions for Assumed Target’s Positions

Cmejla

Kounovsky

Málek

et al. 2018

Latent Variable Analysis and Signal Separation

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Unsupervised spatial dictionary learning for sparse underdetermined multichannel source separation

Cited by 11 publications

References 11 publications

Multiple Speech Source Separation Using Inter-Channel Correlation and Relaxed Sparsity

Multiple Speech Source Separation Using Inter-Channel Correlation and Relaxed Sparsity

Multiple Speech Source Separation with Non-Sparse Components Recovery by Using Dual Similarity Determination

Independent Vector Analysis Exploiting Pre-learned Banks of Relative Transfer Functions for Assumed Target’s Positions

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