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Unsupervised repetition enables rapid perceptual learning.

Vahid Montazeri¹, Michelle R Kapolowicz², Peter F Assmann¹

¹School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas 75080, USA.

The Journal of the Acoustical Society of America

|December 2, 2021

Summary

Repetition helps listeners identify target sounds within complex auditory scenes. Repeated sounds become perceptually salient, enabling recognition of their features, unlike non-repeated sounds.

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Area of Science:

Auditory Perception
Psychoacoustics
Computational Auditory Scene Analysis

Background:

Listeners often face challenges in distinguishing individual sound sources from complex auditory environments.
Understanding auditory scene analysis is crucial for fields like hearing aid development and artificial intelligence.

Purpose of the Study:

To investigate how auditory repetition aids in segregating and identifying target sounds amidst competing novel sounds.
To explore the role of perceptual salience in recognizing sound features based on repetition.
To compare human listener performance with computational models of auditory learning.

Main Methods:

Experiment 1: Replicated previous findings using multivariate Gaussian models to create sound mixtures and assessed identification after exposure.
Experiment 2: Extended the task to recognition of temporal ramps in repeating versus non-repeating sounds.
Computational Modeling: Compared human results with generalized Hebbian and anti-Hebbian learning network models.

Main Results:

Listeners struggled to identify novel sounds in mixtures without specific repetition strategies.
Repetition significantly enhanced the perceptual salience of a target sound, facilitating recognition of its temporal ramp.
A generalized Hebbian learning model mirrored human listener performance, while an anti-Hebbian model showed opposite patterns.

Conclusions:

Auditory repetition is a key mechanism for source segregation and enhancing perceptual salience in complex sound environments.
Hebbian learning principles may underlie the human ability to extract and recognize repeated auditory sources.
These findings have implications for understanding auditory attention and developing more effective auditory processing models.