A framework for testing and comparing binaural models

Abstract

Auditory research has a rich history of combining experimental evidence with computational simulations of auditory processing in order to deepen our theoretical understanding of how sound is processed in the ears and in the brain. Despite significant progress in the amount of detail and breadth covered by auditory models, for many components of the auditory pathway there are still different model approaches that are often not equivalent but rather in conflict with each other. Similarly, some experimental studies yield conflicting results which has led to controversies. This can be best resolved by a systematic comparison of multiple experimental data sets and model approaches. Binaural processing is a prominent example of how the development of quantitative theories can advance our understanding of the phenomena, but there remain several unresolved questions for which competing model approaches exist. This article discusses a number of current unresolved or disputed issues in binaural modelling, as well as some of the significant challenges in comparing binaural models with each other and with the experimental data. We introduce an auditory model framework, which we believe can become a useful infrastructure for resolving some of the current controversies. It operates models over the same paradigms that are used experimentally. The core of the proposed framework is an interface that connects three components irrespective of their underlying programming language: The experiment software, an auditory pathway model, and task-dependent decision stages called artificial observers that provide the same output format as the test subject.

A Physiologically Inspired Model for Solving the Cocktail Party Problem

“…ITD is created when a sound arrives at the more proximal ear earlier than the more distal ear, while ILD is created when the head shadows the more distal ear, decreasing the loudness compared with the more proximal ear. There are many models for binaural cue computation (Dietz et al 2018). We elected to adapt a physiology-based model of the spatial localization network of the barn owl midbrain (Fischer et al 2009), because it is one of the most accurate and best understood physiological systems for sound localization.…”

Population rate-coding predicts correctly that human sound localization depends on sound intensity

“…This read-out is a direct realization of the original canonical rate-based model for ITD decoding (van Bergeijk, 1962). Alternative rate-code readouts exist (for a recent summary of binaural models, see Dietz et al, 2018). Most of these rate-code models rely on subtractive comparisons between populations of neurons that are tuned to opposite hemifields, inherently sharing ambiguous readouts at low suprathreshold sound intensities.…”

Inferring the basis of binaural detection with a modified autoencoder

“…However, understanding of binaural detection in humans has been mired due to ambiguity regarding whether animal neurophysiology data satisfactorily accounts for human psychophysics. There is ongoing debate as to which of a number of theoretical frameworks best relate to human binaural detection [18,30,33,34]. For example, animal neural data lend support to a theory of binaural cross-correlation [14][15][16][17].…”

“…a background noise) by up to 15 dB, represents one such instance. There is ongoing debate as to which of a number of theoretical frameworks best relate to human binaural detection [10][11][12][13][14] . For example, animal neural data lend support to a theory of binaural cross-correlation [15][16][17] .…”