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.
['Naoki Murata', 'Shoichi Koyama', 'Hirokazu Kameoka', 'Norihiro Takamune', 'Hiroshi Saruwatari']
Sparse sound field decomposition with multichannel extension of complex NMF