Modelling Pitch Perception with Adaptive Resonance Theory Artificial Neural Networks

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Abstract
Most modern pitch-perception theories incorporate a pattern-recognition scheme to extract pitch. Typically, this involves matching the signal to be classified against a harmonic-series template for each pitch to find the one with the best fit. Although often successful, such approaches tend to lack generality and may well fail when faced with signals with much depleted or inharmonic components. Here, an alternative method is described, which uses an adaptive resonance theory (ART) artificial neural network (ANN). By training this with a large number of spectrally diverse input signals, we can construct more robust pitch-templates which can be continually updated without having to re-code knowledge already acquired by the ANN. The input signal is Fourier-transformed to produce an amplitude spectrum. A mapping scheme then transforms this to a distribution of amplitude within ‘semitone bins’. This pattern is then presented to an ARTMAP ANN consisting of an ART2 and ART1 unsupervised ANN linked by a map field. The system was trained with pitches ranging over three octaves (C3 to Cf) on a variety of instruments and developed a desirable insensitivity to phase, timbre and loudness when classifying.

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