Abstract
The fundamental process of auditory scene analysis is the organization of elementary acoustic features in a complex auditory scene into grouped meaningful auditory streams. There are two important issues which need to be addressed for modeling auditory scene analysis. The first issue is concerned with the representation of elementary acoustic features, whilst the second issue is related to the binding mechanism. This paper presents a neural model for auditory scene analysis in which a two-dimensional amplitude modulation (AM) map is used to represent elementary acoustic features and the synchronization of neural oscillators is adopted as the binding mechanism. The AM map captures the modulation frequencies of sound signals filtered by an auditory filterbank. Since the modulation frequencies are the F0-related features for voiced speech signals, F0-based segregation can be utilized to group the auditory streams. The grouping of F0-related features is attained as the formation of the synchronization of nonlinear neural oscillators. Each oscillator is associated with a certain modulation frequency. A set of oscillators are synchronized only when their associated frequencies are harmonically related. The proposed model is tested on synthetic double-vowel identification and the results are in accordance with psychophysical data.
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