Concurrent vowel identification. III. A neural model of harmonic interference cancellation

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Abstract
This paper presents a “neural cancellation filter” capable of segregating weak targets from competing harmonic backgrounds, and a model of concurrent vowel segregation based upon it. The elementary cancellation filter comprises a delay line and an inhibitory synapse. Filters within each peripheral channel are tuned to the period of the competing sound to suppress its correlates within the neural discharge pattern. In combination with a pattern matching model based on autocorrelation functions summed over channels, the cancellation filter forms a model of concurrent vowel identification. The model predicts the number of vowels reported for each stimulus (when subjects are allowed to report one or two) and identification rates. It belongs to the class of “harmonic cancellation” models that are supported by experimental evidence that vowel identification is better when competing sounds are harmonic than inharmonic. Two alternative schemes using the same filter are also considered. One derives a “place” representation from the magnitude of the filter output. The other uses the ratio of filter input/output to select channels.

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