Multiple Sound Source Separation by Jointing Single Source Zone Detection and Linearly Constrained Minimum Variance

Liu, Jinxiang; Yang, Qishan; Jia, Maoshen; Zhang, Xinfeng

doi:10.1145/3436369.3437435

Cited by 2 publications

(6 citation statements)

References 11 publications

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“…A high MUSHRA score means better listening quality. The reference methods were the separation algorithm based on independent component analysis (ICA) [10], the separation algorithm based on expectation maximization (EM) [36], the joint sparse and non-sparse components separation method (JSNCS) [14], and the separation method based on linearly constrained minimum variance beamforming (LCMV) [9].…”

Section: Experiments Conditionsmentioning

confidence: 99%

“…Condition "Proposed" is the proposed separation method; condition "JSNCS" is the reference method proposed in [14]. Condition "LCMV" represents the separation method proposed in [9].…”

Section: Objective Evaluationmentioning

confidence: 99%

“…2022, 12, x FOR PEER REVIEW 16 of 20 method proposed in [14]. Condition "LCMV" represents the separation method proposed in [9]. It can be found that the intelligibility of the separated signal obtained by the proposed method is higher than that of the reference method, especially in a high reverberant environment.…”

Section: Objective Evaluationmentioning

confidence: 99%

“…Condition "ICA" and "EM" mean the two reference methods proposed in [10] and [35], respectively. Condition "LCMV" is the reference method in [9].…”

Section: Objective Evaluationmentioning

confidence: 99%

“…Recently, the improved beamforming method has been proposed. 'Single source zone' detection is introduced into linearly constrained minimum variance beamforming to achieve multi-source separation [9]. However, the performance of this method is poor in a high-reverberation environment.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Source Separation Approach Based on DOA Cue and DNN

Jia

Pai

2022

Applied Sciences

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Multiple sound source separation in a reverberant environment has become popular in recent years. To improve the quality of the separated signal in a reverberant environment, a separation method based on a DOA cue and a deep neural network (DNN) is proposed in this paper. Firstly, a pre-processing model based on non-negative matrix factorization (NMF) is utilized for recorded signal dereverberation, which makes source separation more efficient. Then, we propose a multi-source separation algorithm combining sparse and non-sparse component points recovery to obtain each sound source signal from the dereverberated signal. For sparse component points, the dominant sound source for each sparse component point is determined by a DOA cue. For non-sparse component points, a DNN is used to recover each sound source signal. Finally, the signals separated from the sparse and non-sparse component points are well matched by temporal correlation to obtain each sound source signal. Both objective and subjective evaluation results indicate that compared with the existing method, the proposed separation approach shows a better performance in the case of a high-reverberation environment.

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Section: Experiments Conditionsmentioning

confidence: 99%

“…Condition "Proposed" is the proposed separation method; condition "JSNCS" is the reference method proposed in [14]. Condition "LCMV" represents the separation method proposed in [9].…”

Section: Objective Evaluationmentioning

confidence: 99%