SIMULTANEOUS CLUSTERING OF MIXING AND SPECTRAL MODEL PARAMETERS FOR BLIND SPARSE SOURCE SEPARATION

This paper proposes a sparse source separation method which clusters the phase difference between the microphone observations and the amplitude modulation (AM) of the source spectrum simultaneously. The phase difference clustering separates the signals in each frequency bin, and the AM clustering corresponds to permutation alignment. Because the proposed method has an inherent ability to align the permutation of frequency components, the proposed method can be applied even when the spatial aliasing problem occurs. Moreover, because the common AM property collects the synchronized frequency components, we can model the microphone observations with a small number of sources. This property enables us to count the number of sources. That is, the proposed method can be applied even if the number of sources is unknown. The experimental results confirm the effectiveness of our proposed method.