Auditory scene analysis based on time-frequency integration of shared FM and AM

This paper describes a new method for computational auditory scene analysis which is based on 1) waveform operators to extract instantaneous frequency (IF), frequency change (FM), and amplitude change (AM) from subband signals, and 2) a voting method into a probability distribution to extract coherency (shared fundamental frequency, shared FM, and shared AM) involved in them. We introduce non-parametric Kalman filtering for the time-axis integration. A consistent AM operator which is independent to frequency change is newly defined. Sharpness of the resultant probability distribution is examined with relating to the definition of the operators and subband bandwidth. We evaluate the performance of the algorithm by using several speech sounds.