Speech Source Separation Using ICA in Constant Q Transform Domain

Dheeraj Sai D.V.L.N, Kishor K.S, Sri Rama Murty Kodukula

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-time Fourier transform (STFT). The frequency components computed using STFT are separated by constant frequency difference with a constant frequency resolution. However, it is well known that the human auditory mechanism offers better resolution at lower frequencies. Hence, the perceptual quality of the extracted sources critically depends on the separation achieved in the lower frequency components. In this paper, we propose to perform source separation on the time-frequency representation computed though constant Q transform (CQT), which offers 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 constant 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 3 dB or more in the Signal to Interference Ratios of the extracted sources.

 DOI: 10.21437/Interspeech.2018-1732

Cite as: D.V.L.N, D.S., K.S, K., Kodukula, S.R.M. (2018) Speech Source Separation Using ICA in Constant Q Transform Domain. Proc. Interspeech 2018, 846-850, DOI: 10.21437/Interspeech.2018-1732.

  author={Dheeraj Sai D.V.L.N and Kishor K.S and Sri Rama Murty Kodukula},
  title={Speech Source Separation Using ICA in Constant Q Transform Domain},
  booktitle={Proc. Interspeech 2018},