A New Approach to Model Pitch Perception Using Sparse Coding

Abstract

Our acoustical environment abounds with repetitive sounds, some of which are related to pitch perception. It is still unknown how the auditory system, in processing these sounds, relates a physical stimulus and its percept. Since, in mammals, all auditory stimuli are conveyed into the nervous system through the auditory nerve (AN) fibers, a model should explain the perception of pitch as a function of this particular input. However, pitch perception is invariant to certain features of the physical stimulus. For example, a missing fundamental stimulus with resolved or unresolved harmonics, or a low and high-level amplitude stimulus with the same spectral content–these all give rise to the same percept of pitch. In contrast, the AN representations for these different stimuli are not invariant to these effects. In fact, due to saturation and non-linearity of both cochlear and inner hair cells responses, these differences are enhanced by the AN fibers. Thus there is a difficulty in explaining how pitch percept arises from the activity of the AN fibers. We introduce a novel approach for extracting pitch cues from the AN population activity for a given arbitrary stimulus. The method is based on a technique known as sparse coding (SC). It is the representation of pitch cues by a few spatiotemporal atoms (templates) from among a large set of possible ones (a dictionary). The amount of activity of each atom is represented by a non-zero coefficient, analogous to an active neuron. Such a technique has been successfully applied to other modalities, particularly vision. The model is composed of a cochlear model, an SC processing unit, and a harmonic sieve. We show that the model copes with different pitch phenomena: extracting resolved and non-resolved harmonics, missing fundamental pitches, stimuli with both high and low amplitudes, iterated rippled noises, and recorded musical instruments.

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

“…Although mechanistic explanations of pitch perception have been widely discussed over many decades (Licklider, 1951;Schouten et al, 1962;Goldstein, 1973;Wightman, 1973;Terhardt, 1979;Slaney and Lyon, 1990;Meddis and Hewitt, 1991;Meddis and O'Mard, 1997;Shamma and Klein, 2000;Laudanski et al, 2014;Barzelay et al, 2017), there have been few attempts to explain pitch in normative terms. But like other aspects of perception, pitch is plausibly the outcome of an optimization process (realized through some combination of evolution and development) that produces good performance under natural conditions.…”

“…One factor limiting the resolution of these debates is that previous models of pitch have generally not attained quantitatively accurate matches to human behavior (Licklider, 1951; Schouten et al, 1962; Goldstein, 1973; Wightman, 1973; Terhardt, 1979; Slaney and Lyon, 1990; Meddis and Hewitt, 1991; Meddis and O’Mard, 1997; Shamma and Klein, 2000; Bernstein and Oxenham, 2005; Laudanski et al, 2014; Ahmad et al, 2016; Barzelay et al, 2017). Moreover, because most previous models have been mechanistic rather than normative, they were not optimized for their task, and thus do not speak to the potential adaptation of pitch perception for particular types of sounds or peripheral neural codes.…”

Periodicity Pitch Perception

“…We like to show a bio-plausible way of F0 estimation as a possible starting point for novel research. As a prerequisite, auditory models of pitch perception have been created, implemented, and discussed (Patterson et al, 2002;Laudanski et al, 2014;Langner, 2015;Stolzenburg, 2015;Ahmad et al, 2016;Joris, 2016;McLachlan, 2016;Barzelay et al, 2017;Friedrichs et al, 2017;Saeedi et al, 2017;Tang et al, 2017;Todd et al, 2017;Harczos and Klefenz, 2018;Oxenham, 2018;Peng et al, 2018).…”

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

“…Neuro-physiologically parameterized auditory models mimic the dynamics of the basilar membrane, the mechano-electrical coupling of inner hair cells, and the membrane voltage regulated vesicle rate-kinetics (Voutsas et al, 2005 ; Balaguer-Ballester et al, 2009 ). Several pitch decoders are constructed as neural networks (Ahmad et al, 2016 ; Barzelay et al, 2017 ). Some recent pitch decoders are realized as spiking neural networks in which Spike-Timing Dependent Plasticity (STDP) learning rules are applied (Saeedi et al, 2016 , 2017 ).…”