Title :
Monaural speech segregation based on pitch tracking and amplitude modulation
Author :
Hu, Guoning ; Wang, DeLiang
Author_Institution :
Biophys. Program, Ohio State Univ., Columbus, OH, USA
Abstract :
Segregating speech from one monaural recording has proven to be very challenging. Monaural segregation of voiced speech has been studied in previous systems that incorporate auditory scene analysis principles. A major problem for these systems is their inability to deal with the high-frequency part of speech. Psychoacoustic evidence suggests that different perceptual mechanisms are involved in handling resolved and unresolved harmonics. We propose a novel system for voiced speech segregation that segregates resolved and unresolved harmonics differently. For resolved harmonics, the system generates segments based on temporal continuity and cross-channel correlation, and groups them according to their periodicities. For unresolved harmonics, it generates segments based on common amplitude modulation (AM) in addition to temporal continuity and groups them according to AM rates. Underlying the segregation process is a pitch contour that is first estimated from speech segregated according to dominant pitch and then adjusted according to psychoacoustic constraints. Our system is systematically evaluated and compared with pervious systems, and it yields substantially better performance, especially for the high-frequency part of speech.
Keywords :
acoustic signal processing; amplitude modulation; correlation methods; harmonics; speech enhancement; amplitude modulation; auditory scene analysis; cross-channel correlation; harmonics; monaural speech segregation; pitch contour; pitch tracking; temporal continuity; voice speech segregation; Amplitude modulation; Automatic speech recognition; Hidden Markov models; Image analysis; Interference; Power harmonic filters; Psychology; Sensor arrays; Speech analysis; Speech enhancement; AM; Amplitude modulation; computational auditory scene analysis; grouping; monaural speech segregation; pitch tracking; segmentation;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2004.832812