Sparse sound field decomposition with multichannel extension of complex NMF
Sparse sound field decomposition with multichannel extension of complex NMF
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DOI：
10.1109/icassp.2016.7471694
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Naoki Murata;Shoichi Koyama;H. Kameoka;Norihiro Takamune;H. Saruwatari
中科院分区：
文献类型：
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作者：
Naoki Murata;Shoichi Koyama;H. Kameoka;Norihiro Takamune;H. Saruwatari
提出了一种基于时频域源信号先验信息的稀疏声场分解方法。稀疏声场分解已被证明是一种有效的声信号处理方法。目前的稀疏分解方法仅基于源分布的空间稀疏性。但是，可以假设可能要分解的源信号是事先近似已知的。为了利用这些先验信息，我们将复非负因子分解模型引入稀疏声场分解中。由于可以提前训练可能的源信号的幅度谱，因此即使在源信号高度相关和源处于高噪声环境下，也可以提高稀疏分解的精度。此外，利用辅助函数法推导了所提出的分解算法。数值实验表明，该方法显著提高了稀疏分解的性能。
A sparse sound field decomposition method using prior information on source signals in the time-frequency domain is proposed. Sparse sound field decomposition has been proved to be effective for various acoustic signal processing applications. Current methods for sparse decomposition are based only on the spatial sparsity of the source distribution. However, it can be assumed that possible source signals to be decomposed are approximately known in advance. To exploit this prior information, we incorporated the complex nonnegative factorization model into sparse sound field decomposition. Since the magnitude spectrum of the possible source signals can be trained in advance, accuracy of the sparse decomposition can be improved even when the source signals are highly correlated and the sources are in a highly noisy environment. In addition, the proposed decomposition algorithm is derived using the auxiliary function method. Numerical experiments indicated that the sparse decomposition performance was significantly improved using the proposed method.