A cascade autocorrelation model of pitch perception

Abstract

Autocorrelation algorithms, in combination with computational models of the auditory periphery, have been successfully used to predict the pitch of a wide range of complex stimuli. However, new stimuli are frequently offered as counterexamples to the viability of this approach. This study addresses the issue of whether in the light of these challenges the predictive power of autocorrelation can be preserved by changes to the peripheral model and the computational algorithm. An existing model is extended by the addition of a low-pass filter of the summary integration of the individual within-channel autocorrelations. Other recent developments are also incorporated, including nonlinear processing on the basilar membrane and the use of integration time constants that are proportional to the autocorrelation lags. The modified and extended model predicts with reasonable success the pitches of a range of stimuli that have proved problematic for earlier implementations of the autocorrelation principle. The evaluation stimuli include short tone sequences, click trains consisting of alternating interclick intervals, click trains consisting of mixtures of regular and irregular intervals, shuffled click trains, and transposed tones.