Particle filtering by sigmoidal weight update for speech pitch correction

We propose a pitch track correction technique using a sigmoidal objective function in a particle filtering framework. The conventional method for pitch track correction simply considers the peak locations of the autocorrelation functions as the pitch values and depends only on the longest reliable pitch streak. This constraint may induce pitch correction errors. The proposed approach uses particle filtering to further correctly track the pitch values. To apply the particle filtering algorithm to pitch track correction, a method for the importance weight computation using a sigmoidal function on foreground streams is proposed. The similarity between the the estimated pitch values and the subband signals are computed, and a sigmoid transfer function is used to convert it as a probability value. To verify the efficiency of our proposed approach, we carried out speech segregation experiments for mixtures of speech and various noise sources in various mixing signal-to-noise ratios (SNRs). With respect to several performance measures including SNR, energy loss ratio, and noise residue ratio of the segregated speech, the proposed method achieved superior performance compared to the conventional approach.