A Pitch Detection Method for Speech Signals with Low Signal-to-Noise Ratio

A new method for the pitch detection of speech severely degraded by a white noise is presented in this paper. We intend to incorporate a noise reduction approach based on a modified power spectral subtraction scheme to enhance the pre-processed speech prior to pitch estimation. The de-noised speech is then passed through an inverse filter, whose parameters are derived from the linear prediction (LP) analysis, yielding an output referred to as the LP residual. Since the LP residual is capable of delivering the knowledge of glottal closure events, it is utilized to propose a new average magnitude sum function (AMSF) and an average magnitude difference function (AMDF) both of which exhibit the periodicity at the pitch period. Exploiting the property that the AMDF shows a notch while the AMSF produces a peak, the AMDF is weighted by the reciprocal of the AMSF to reinforce the pitch-harmonic-notches in a heavy noise. Simulation results using the Keele database guarantee a superior pitch detection efficacy of the proposed approach for a white noise-corrupted speech compared to some of the existing methods at a very low signal-to-noise ratio (SNR).