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首页> 外文会议>2016 IEEE International Workshop on Acoustic Signal Enhancement >Sparseness-based multichannel nonnegative matrix factorization for blind source separation
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Sparseness-based multichannel nonnegative matrix factorization for blind source separation

机译：基于稀疏度的多通道非负矩阵分解用于盲源分离

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This paper deals with the problem of audio source separation using multichannel observation. Utilizing the sparseness of sound signals in the time-frequency domain is a successful approach to source separation that enables us to perform separation based on spatial features obtained from a microphone array. On the other hand, nonnegative matrix factorization (NMF) is also a promising approach for audio source separation, which performs separation based on spectral features. This paper incorporates the idea of NMF into sparseness-based source separation and proposes a novel approach to multichannel source separation based on both spatial and spectral features. Experimental results reveal that our proposed method improves the signal-to-distortion ratio (SDR) by 0.26 dB and the signal-to-interference ratio (SIR) by 1.96 dB compared with a conventional sparseness-based approach. In addition, our proposed model eliminates the need for a number of matrix inversions thanks to the sparseness assumption, and thereby requires a much lower computational cost than a previously-proposed multichannel NMF approach, which also utilizes spectral and spatial features.
机译：本文探讨了使用多通道观测进行音频源分离的问题。在时频域中利用声音信号的稀疏性是一种成功的信号源分离方法，它使我们能够基于从麦克风阵列获得的空间特征进行分离。另一方面，非负矩阵分解（NMF）也是一种有前途的音频源分离方法，该方法基于频谱特征执行分离。本文将NMF的思想纳入基于稀疏性的信号源分离中，并提出了一种基于空间和光谱特征的多通道信号源分离新方法。实验结果表明，与传统的基于稀疏的方法相比，我们提出的方法将信号失真比（SDR）提高了0.26 dB，将信号干扰比（SIR）提高了1.96 dB。另外，由于稀疏性假设，我们提出的模型消除了对大量矩阵求逆的需要，因此与先前提出的也利用频谱和空间特征的多通道NMF方法相比，所需的计算成本低得多。

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