Pitch tracking based on statistical anticipation

An effective multipitch tracking algorithm for noisy speech is critical for auditory processing. However, the performance of existing algorithms is not satisfactory. We have developed a robust algorithm for multipitch tracking of noisy speech based on statistical anticipation. By combining an improved channel and peak selection method, a new integration method for extracting periodicity information across the different channels, and a hidden Markov model (HMM) for forming continuous pitch tracks, our algorithm can reliably track single and double pitch tracks in a noisy environment