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    • Cognitive & Neural Systems
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    A Neural Network Model of Auditory Scene Anaysis and Source Segregation

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    Date Issued
    1994-12
    Author(s)
    Govindarajan, Krishna
    Grossberg, Stephen
    Wyse, Lonce
    Cohen, Michael
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    Permanent Link
    https://hdl.handle.net/2144/2177
    Abstract
    In environments with multiple sound sources, the auditory system is capable of teasing apart the impinging jumbled signal into different mental objects, or streams, as in its ability to solve the cocktail party problem. A neural network model of auditory scene analysis, called the ARTSTREAM model, is presented that groups different frequency components based on pitch and spatial location cues, and selectively allocates the components to different streams. The grouping is accomplished through a resonance that develops between a given object's pitch, its harmonic spectral components, and (to a lesser extent) its spatial location. Those spectral components that are not reinforced by being rnatched with the top-down prototype read-out by the selected object's pitch representation are suppressed, thereby allowing another stream to capture these components, as in the "old-plus-new heuristic" of Bregman. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, mechanisms. The model is used to simulate data from psychophysical grouping experiments, such as how a. tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone clue to proximity in frequency, even if noise replaces the tones at their intersection point. The model also simulates illusory auditory percepts such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. The stream resonances provide the coherence that allows one voice or instrument to be tracked through a multiple source environment.
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    Copyright 1994 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.
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    • CAS/CNS Technical Reports [485]


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