Pitch Tracking for Model-Based Speech Separation

Estimating multiple pitch frequencies of concurrent speech sources from a single-microphone input is essential to speech separation. Nevertheless, pitch cues of individual sources are weakened by each other, making the estimation unreliable. This paper presents a pitch tracking method that incorporated in a model-based separation framework. Multiple pitch estimation is simplified into single pitch estimation by segregating the source envelope from mixture spectrum with statistics of familiar speech patterns. Comprehensive experiments have compared the proposed tracking method with a recently reported multiple pitch estimator and its modified version equipped with ideal pitch cues. Lower estimation errors are achieved. Furthermore, this approach is applicable to other model-based frameworks as well.