Pseudo-determined blind source separation for ad-hoc microphone networks
IEEE/ACM Transactions on Audio, Speech and Language Processing
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We propose a pseudo-determined blind source separation framework that exploits the information from a large number of microphones in an ad-hoc network to extract and enhance sound sources in a reverberant scenario. After compensating for the time offsets and sampling rate mismatch between (asynchronous) signals, we interpret as a determined M×M mixture the over-determined M×N mixture, where M>N is the number of microphones and N is the number of sources. Next, we propose a pseudo-determined mixture model that can apply an M×M independent component analysis (ICA) directly to the M-channel recordings. Moreover, we propose a reference-based permutation alignment scheme that aligns the permutation of the ICA outputs and classifies them into target channels, which contain the N sources, and non-target channels, which contain reverberation residuals. Finally, using the signals from non-target channels, we estimate in each target channel the power spectral density of the noise component that we suppress with a spectral post-filter. Interestingly, we also obtain late-reverberation suppression as by-product. Experiments show that each processing block improves incrementally source separation and that the performance of the proposed pseudo-determined separation improves as the number of microphones increases.