Speech Source Separation Using ICA in Constant Q Transform Domain

In order to separate individual sources from convoluted speech mixtures, complex-domain independent component analysis (ICA) is employed on the individual frequency bins of time-frequency representations of the speech mixtures, obtained using short term Fourier transform (STFT). The frequency components computed using STFT are separated by constant frequency di�erence with a constant frequency resolution. However, it is well known that the human auditory mechanism o�ers better resolution at lower frequencies. Hence, the perceptual quality of the extracted sources critically depends on the separation achieved in the lower frequency components. A method has been proposed to perform source separation on the time-frequency representation computed though constant Q transform, which o�ers non uniform logarithmic binning in the frequency domain. Complex-domain ICA is performed on the individual bins of the CQT in order to get separated components in each frequency bin which are suitably scaled and permuted to obtain separated sources in the CQT domain. The estimated sources are obtained by applying inverse Q transform to the scaled and permuted sources. In comparison with the STFT based frequency domain ICA methods, there has been a consistent improvement of 3dB or more in the Signal to Interference Ratios of the extracted sources. vi