A method for sparse sound field decomposition with parametric dictionary learning is proposed. Sound field decomposition forms the foundation of various acoustic signal processing applications. Our main focus is sound field recording and reproduction for high-fidelity audio systems. To improve the reproduction accuracy above the spatial Nyquist frequency, determined by the intervals between array elements, i.e., super-resolution in recording and reproduction, We have proposed a method based on sparse sound field decomposition. For more accurate decomposition, we propose a method for parametric dictionary learning to adaptively optimize the dictionary parameters using input signals. The proposed method is based on Newton's method including pruning of the columns of a dictionary matrix. Numerical simulation results indicate that more accurate sparse decomposition is achieved by the proposed method. The reproduction accuracy is also improved above the spatial Nyquist frequency.
['Naoki Murata', 'Shoichi Koyama', 'Norihiro Takamune', 'Hiroshi Saruwatari']
Sparse sound field decomposition with parametric dictionary learning for super-resolution recording and reproduction