A multipitch tracking algorithm for noisy and reverberant speech
Zhaozhang Jin, DeLiang Wang · 2010
Determining multiple pitches in noisy and reverberant speech is an important and challenging task. We propose a robust multipitch tracking algorithm in the presence of both background noise and room reverberation. A new channel selection method is utilized in conjunction with an auditory front-end to extract periodicity features in the time-frequency space. These features are combined to formulate frame level conditional probabilities given each pitch state. A hidden Markov model is then applied to integrate these probabilities and search for the most likely pitch state sequences. The proposed approach can reliably detect up to two simultaneous pitch contours in noisy and reverberant conditions. Quantitative evaluations show that our system significantly outperforms existing ones, particularly in reverberant environments.