Licklider [Expcrientia 7, 128-133 (1951 } ] presented a theory of pitch highlighting the role of auditory-nerve interspike-interval timing information in the process of pitch extraction. His theory is simplified and amended and presented here as a computer implementation. This implementation has been successfully tested using simulations of a wide range of classical demonstrations of pitch phenomena including the missing fundamental, ambiguous pitch, pitch shift of equally spaced, inharmonic components, musical chords, repetition pitch, the pitch of interrupted noise, the existence region, and the dominance region for pitch. The theory is compared with a number of alternative theories and the physiological plausibility of a temporal model is considered.

Human listeners are better able to identify two simultaneous vowels if the fundamental frequencies of the vowels are different. A computational model is presented which, for the first time, is able to simulate this phenomenon at least qualitatively. The first stage of the model is based upon a bank of bandpass filters and inner hair-cell simulators that simulate approximately the most relevant characteristics of the human auditory periphery. The output of each filter/hair-cell channel is then autocorrelated to extract pitch and timbre information. The pooled autocorrelation function (ACF) based on all channels is used to derive a pitch estimate for one of the component vowels from a signal composed of two vowels. Individual channel ACFs showing a pitch peak at this value are combined and used to identify the first vowel using a template matching procedure. The ACFs in the remaining channels are then combined and used to identify the second vowel. Model recognition performance shows a rapid improvement in correct vowel identification as the difference between the fundamental frequencies of two simultaneous vowels increases from zero to one semitone in a manner closely resembling human performance. As this difference increases up to four semitones, performance improves further only slowly, if at all.

Editor's Note.' Original contributions to the Technical Notes and Research Briefs section are always welcome. Manuscripts should be double-spaced, and ordinarily not longer than about 1500 words. There are no publication charges, and consequently, no free reprints; however, reprints may be purchased at the usual prices. Implementation details of a computation model of the inner hair-cell/auditory-nerve synapse [43.64.Bt, 43.64.Pg, 43.64.Nf]

A computational model of binaural lateralization is described. An accurate model of the auditory periphery feeds a tonotopically organized multichannel cross-correlation mechanism. Lateralization predictions are made on the basis of the integrated activity across frequency channels. The model explicitly weights cross-correlation peaks closer to the center preferentially, and effectively weights information that is consistent across frequencies more heavily because they have a greater impact in the across frequency integration. This model is complementary to the weighted-image model of Stem et al. [J. Acoust. Soc. Am. 84, 156-165 (1988) ], although the model described in this paper is physiologically more plausible, is simpler, and is more versatile in the range of input stimuli that are possible.

A computer model is presented of a neural circuit that replicates amplitude-modulation (AM) sensitivity of cells in the central nucleus of the inferior colliculus (ICC). The ICC cell is modeled as a point neuron whose input consists of spike trains from a number of simulated ventral cochlear nucleus (VCN) chopper cells. Input to the VCN chopper cells is provided by simulated spike trains from a model of the auditory periphery [Hewitt et al., J. Acoust. Soc. Am. 91, 2096-2109 (1992)]. The performance of the model at the output of the auditory nerve, the cochlear nucleus and ICC simulations in response to amplitude-modulated stimuli is described. The results are presented in terms of both temporal and rate modulation transfer functions (MTFs) and compared with data from physiological studies in the literature. Qualitative matches were obtained to the following main empirical findings: (a) Auditory nerve temporal-MTFs are low pass, (b) VCN chopper temporal-MTFs are low pass at low signal levels and bandpass at moderate and high signal levels, (c) ICC unit temporal-MTFs are low pass at low signal levels and broadly tuned bandpass at moderate and high signal levels, and (d) ICC unit rate-MTFs are sharply tuned bandpass at low and moderate signal levels and flat at high levels. VCN and ICC units preferentially sensitive to different rates of modulation are presented. The model supports the hypothesis that cells in the ICC decode temporal information into a rate code [Langner and Schreiner, J. Neurophysiol. 60, 1799-1822 (1988)], and provides a candidate wiring diagram of how this may be achieved.