Error Signal Based Method for Real Time Pitch Extraction from Noisy Speech
TL;DR
A Modified Weighted Autocorrelation Function (MWAF) executed in the Linear Predictive Coding (LPC) residual domain, instead of directly executing over the noisy source speech signal, offering a stable solution for pitch tracking in degraded acoustic conditions.
Abstract
Pitch estimation efficiency always gets affected by two major phenomena. The first one is additive noise which by nature are not correlated with the source speech signal. The second one is the vocal tract effect which creates unnecessary peaks within the signal waveform, resulting inaccurate detection of the pitch value. This paper proposes a Modified Weighted Autocorrelation Function (MWAF) executed in the Linear Predictive Coding (LPC) residual domain, instead of directly executing over the noisy source speech signal. Converting the noisy source speech signal to LP residual helps eliminating the vocal tract effects and emphasizing the necessary effects of glottal excitation. The Autocorrelation Function (ACF) and the Circular Average Magnitude Difference Function (CAMDF) are directly applied on this residual. The proposed MWAF is then calculated by multiplying the ACF with the reciprocal of the CAMDF, which enhances the periodic components as well as reduces the noise induced false peaks. Experimental results were evaluated on the KEELE and NTT databases across White, Babble, Train and HF Channel noise conditions. The result shows that our proposed algorithm reduces the Gross Pitch Error (GPE) by an average of 40.34% compared to baseline methods such as BaNa, PEFAC and WAF. Furthermore, the algorithm achieves an overall computational gain of 53.44% compared to BaNa, PEFAC and WAF, offering a stable solution for pitch tracking in degraded acoustic conditions.