Optimum harmonics tracking filter for auditory scene analysis

We propose a new harmonics tracking algorithm for auditory scene analysis. It extracts the most significant stream from multiple sound streams as well as the tracking of each harmonic components. For the optimum tracking of pitch candidates, a novel estimation technique based on nonlinear Kalman filtering and Parzen estimate (non-parametric Kalman filter, NPKF) is used. For invariant reconstruction of harmonic components against pitch estimation error, we perform a filtering process in the wavelet domain. Using simulated data and real world data, we show several experimental results for extracting the most dominant sound stream among multiple ones