Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells.

Tamas Harczos,Frank Markus Klefenz

doi:10.3389/fnins.2018.00660

Tamas Harczos, Frank Markus Klefenz

Open Access

https://doi.org/10.3389/fnins.2018.00660

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Abstract

Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch.

Highlights

Sensation of pitch is a perceptual category
The auditory encoder as well as the simulation of the biophysical model of the pitch estimation have been implemented on a PC platform
We mathematically modeled its functionality in the time domain while executing local Hough-transforms in their receptive fields to compensate for latency-phase trajectories

Summary

Introduction

Sensation of pitch is a perceptual category. Pitches are for instance reproducibly generated by music instruments or singing voices, and are notated in musical notes. Each note is assigned a fundamental frequency F0 by reference to the root tone and tuning system. Pitch sensations are evoked by tonal audio data segments as sinusoids, or sinusoids with resolved and unresolved harmonics (even in the case of missing fundamental frequency), and iterated ripple noise (Huang and Rinzel, 2016). Computational pitch models need to be able to generate pitch hypotheses, which can be compared to the annotated ground truth of the audio source data. Various computational pitch models have been compared in a common evaluation matrix and transparently benchmarked in international open contests (Downie, 2008; Cunningham et al, 2017)

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