Improved single-channel speech separation using sinusoidal modeling

We present a novel single-channel separation approach to improve the separation performance while recovering the signals from a mixture. The key idea in this research is to employ a mixture estimator based on unconstrained modified sinusoidal parameters. Compared to the mixmax (binary mask) and Wiener filter (softmask) approaches, the proposed approach works independently of pitch estimates. Furthermore, it is observed that it can achieve acceptable perceptual speech quality with less cross-talk at different signal-to-signal ratios while bringing down the complexity by replacing STFT with sinusoidal parameters. Improvements made by the proposed approach are demonstrated by employing PESQ as our objective measure and MUSHRA listening test as our subjective evaluation.