Verification of the optimal probabilistic basis of aural processing in pitch of complex tones

Abstract

Periodicity pitch for complex tones was quantitatively accounted for by a 2-stage process of Fourier-frequency analysis subject to random errors and significant nonlinearities, followed by an harmonic pattern recognizer that makes an optimum probabilistic estimate of the fundamental period of musical and speech sounds. The theory predicts that periodicity pitch is a multimodal probabilistic function of a given stimulus. A clear and empirically supported distinction is made between limitations on the pitch mechanism caused by the stochastic nature of aural frequency representation and by the deterministic resolution bandwidths of aural frequency analysis. This model was developed earlier (Goldstein, 1973) to account for probabilistic data on pitch errors measured with periodic stimuli comprising 2 successive harmonics. New predictions by the theory were presented that were calculated, with computer simulation where needed, for known probabilistic pitch data from stimuli comprising 3 to 6 successive harmonics. Predicted pitch errors increase with increasing errors in estimating the frequencies of stimulus harmonics and decrease as more harmonics are added to the stimulus. Optimum processor theory fully accounts for the multicomponent pitch data on the basis of similar errors in estimating component stimulus frequencies as reported earlier, thus providing further evidence for the optimum probabilistic basis of [human] aural signal processing in pitch of complex tones.