Monaural separation of independent acoustical components

The problem of blindly separating signal mixtures with fewer mixture components than independent signal sources is mathematically ill-defined, and requires suitable prior information on the nature of the sources. Recently, it has been shown that sparse methods for function approximation using a Laplacian prior can be effective, but the method fails to separate a single mixture without further prior information. Other techniques track harmonics, but assume separability in the time-frequency domain. We show that a measure of temporal and spectral coherence provides an effective cue for separating independent acoustical or sonar sources, in the absence of spatial cues in the monaural case. The technique is shown to successfully separate single mixtures of sources with significant spectral overlap