Predictive analysis of two tone stream segregation via extended Kalman filter

Hearing engages in a seemingly effortless way, complex processes that allow our brains to parse the acoustic environment around us into perceptual sound objects, in a phenomenon called streaming or stream segregation. In this paper, we explore the hypothesis that the auditory system relies on the regularity inherent to each stream to segregate it from other competing streams in the scene. Tracking these regularities is achieved via a recursive prediction that tracks the evolution of each stream, using a Kalman filtering approach. The proposed approach combines spectral analysis operating at the level of the auditory periphery with a temporal analysis using Kalman tracking. To incorporate nonlinear relationships in the signal patterns, we employ an extended Kalman filter. This scheme is tested on sinusoidal patterns, or the two tone paradigm. The combined spectral and temporal analysis developed here is able to predict perceptual results of stream segregation by human listeners in a two tone paradigm.