Fast source separation based on selection of effective temporal frames

A faster computational method for performing frequency domain independent component analysis (FDICA) using a dodecahedral microphone array is proposed. Source separation with FDICA uses the spectrum of observed signals and estimates separation filters for each frequency. However, this technique is complex and requires high computational resources. In this paper, a method of selecting temporal frames which are effective for training the separation filters is proposed and evaluated. The log power spectrum and the kurtosis of amplitude distribution are employed as selection criteria. Performance was evaluated by comparing signal-to-interference performance with that of the conventional method. Experimental results showed that the proposed method reduced computation to 17.1 % of that required by the conventional method, and that separation performance of the proposed method is superior. Therefore, the proposed method can achieve faster computation with lower computational complexity, and its effectiveness can be confirmed.