A model of pitch perception, called the spatial pitch network or SPINET model, is developed and analyzed. The model neurally instantiates ideas from the spectral pitch modeling literature and joins them to basic neural network signal processing designs to stimulate a broader range of perceptual pitch data than previous spectral models. The components of the model are interpreted as peripheral mechanical and neural processing stages, which are capable of being incorporated into a larger network architecture for separating multiple sound sources in the environment. The core of the new model transforms a spectral representation of an acoustic source into a spatial distribution of pitch strengths. The SPINET model uses a weighted "harmonic sieve" whereby the strength of activation of a given pitch depends upon a weighted sum of narrow regions around the harmonics of the nominal pitch value, and higher harmonics contribute less to a pitch than lower ones. Suitably chosen harmonic weighting functions enable computer simulations of pitch perception data involving mistuned components, shifted harmonics, and various types of continuous spectra including rippled noise. It is shown how the weighting functions produce the dominance region, how they lead to octave shifts of pitch in response to ambiguous stimuli, and how they lead to a pitch region in response to the octave-spaced Shepard tone complexes and Deutsch tritones without the use of attentional mechanisms to limit pitch choices. An on-center off-surround network in the model helps to produce noise suppression, partial masking, and edge pitch. Finally, it is shown how peripheral filtering and short-term energy measurements produce a model pitch estimate that is sensitive to certain component phase relationships.

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