A Multi-resolution approach to Common Fate-based audio separation

We propose the Multi-resolution Common Fate Transform (MCFT), a signal representation that increases the separability of audio sources with significant energy overlap in the time-frequency domain. The MCFT combines the desirable features of two existing representations: the invertibility of the recently proposed Common Fate Transform (CFT) and the multi-resolution property of the cortical stage output of an auditory model. We compare the utility of the MCFT to the CFT by measuring the quality of source separation performed via ideal binary masking using each representation. Experiments on harmonic sounds with overlapping fundamental frequencies and different spectro-temporal modulation patterns show that ideal masks based on the MCFT yield better separation than those based on the CFT.