Study on Adaptive Comb filter for Auditory Scene Analysis

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In this study, we have developed optimal filters for quasi-periodic signals involving amplitude and pitch fluctuations (e.g., a real speech or instrumental sound) as a fundamental technology for realization of the auditory scene analysis.First, we derived linear optimal filters based on the quasi-periodic signal model. The Wiener filter solution under the steady-state condition leads to the constant-BW (-bandwidth)/constant-Q mixed comb structure in which each pass-band located at harmonic frequencies has a constant bandwidth in lower frequency, constant-Q characteristic in higher frequency, and those mixture appears in the intermediate frequency. It is shown that such filter banks closely imitates that of the human auditory system. Next, we derived a Kalman filter based on the Ito stochastic differential equation involving unknown random fluctuations as well as the given slowly varying fluctuations. It is verified that the steady-state solution coincides with the Wiener filter one.In addition, we have designed digital filters, which have not only a comb structure for extracting each harmonic component but also a notch structure for suppressing interference between harmonics components, based on digitization of the continuous Kalman filter equation. Finally, we have established a filter system for extracting a specific sound from among the mixed sound, and confirmed the validity of utilizing as the front-end processing for speech recognition.