Illusory sound texture reveals multi-second statistical completion in auditory scene analysis

Abstract Sound sources in the world are experienced as stable even when intermittently obscured, implying perceptual completion mechanisms that “fill in” missing sensory information. We demonstrate a filling-in phenomenon in which the brain extrapolates the statistics of background sounds (textures) over periods of several seconds when they are interrupted by another sound, producing vivid percepts of illusory texture. The effect differs from previously described completion effects in that 1) the extrapolated sound must be defined statistically given the stochastic nature of texture, and 2) the effect lasts much longer, enabling introspection and facilitating assessment of the underlying representation. Illusory texture biases subsequent texture statistic estimates indistinguishably from actual texture, suggesting that it is represented similarly to actual texture. The illusion appears to represent an inference about whether the background is likely to continue during concurrent sounds, providing a stable statistical representation of the ongoing environment despite unstable sensory evidence.

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Illusory sound texture reveals multi-second statistical completion in auditory scene analysis

10.1101/681965 ◽

2019 ◽

Author(s):

Richard McWalter ◽

Josh H. McDermott

Keyword(s):

Sensory Information ◽

Auditory Scene Analysis ◽

Scene Analysis ◽

Sound Sources ◽

Stochastic Nature ◽

Perceptual Completion ◽

Auditory Scene ◽

Stable Representation ◽

Sensory Evidence ◽

The Brain

AbstractSound sources in the world are experienced as stable even when intermittently obscured, implying perceptual completion mechanisms that “fill in” missing sensory information. We demonstrate a filling-in phenomenon in which the brain extrapolates the statistics of background sounds (textures) over periods of several seconds when they are interrupted by another sound, producing vivid percepts of illusory texture. The effect differs from previously described completion effects in that 1) the extrapolated sound must be defined statistically given the stochastic nature of texture, and 2) in lasting much longer, enabling introspection and facilitating assessment of the underlying representation. Illusory texture appeared to be integrated into texture statistic estimates indistinguishably from actual texture, suggesting that it is represented similarly to actual texture. The illusion appears to represent an inference about whether the background is likely to continue during concurrent sounds, providing a stable representation of the environment despite unstable sensory evidence.

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The origins of music in auditory scene analysis and the roles of evolution and culture in musical creation

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2014.0089 ◽

2015 ◽

Vol 370 (1664) ◽

pp. 20140089 ◽

Cited By ~ 21

Author(s):

Laurel J. Trainor

Keyword(s):

Auditory Processing ◽

Social Power ◽

Auditory Scene Analysis ◽

Scene Analysis ◽

Evolutionary Adaptation ◽

Motor Pathways ◽

Musical Adaptations ◽

Auditory Scene ◽

The Brain ◽

Timing Structure

Whether music was an evolutionary adaptation that conferred survival advantages or a cultural creation has generated much debate. Consistent with an evolutionary hypothesis, music is unique to humans, emerges early in development and is universal across societies. However, the adaptive benefit of music is far from obvious. Music is highly flexible, generative and changes rapidly over time, consistent with a cultural creation hypothesis. In this paper, it is proposed that much of musical pitch and timing structure adapted to preexisting features of auditory processing that evolved for auditory scene analysis (ASA). Thus, music may have emerged initially as a cultural creation made possible by preexisting adaptations for ASA. However, some aspects of music, such as its emotional and social power, may have subsequently proved beneficial for survival and led to adaptations that enhanced musical behaviour. Ontogenetic and phylogenetic evidence is considered in this regard. In particular, enhanced auditory–motor pathways in humans that enable movement entrainment to music and consequent increases in social cohesion, and pathways enabling music to affect reward centres in the brain should be investigated as possible musical adaptations. It is concluded that the origins of music are complex and probably involved exaptation, cultural creation and evolutionary adaptation.

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Modeling Grouping Cues for Auditory Scene Analysis Using a Spectral Clustering Formulation

Machine Audition ◽

10.4018/978-1-61520-919-4.ch002 ◽

2010 ◽

pp. 22-60

Author(s):

Luís Gustavo Martins ◽

Mathieu Lagrange ◽

George Tzanetakis

Keyword(s):

Prior Knowledge ◽

Spectral Clustering ◽

Current Knowledge ◽

Auditory Scene Analysis ◽

Scene Analysis ◽

Source Characterization ◽

Sound Sources ◽

Auditory Scene ◽

Concrete Realization ◽

Main Components

Computational Auditory Scene Analysis (CASA) is challenging problem for which many different approaches have been proposed. These approaches can be based on statistical and signal processing methods such as Independent Component Analysis or can be based on our current knowledge about human auditory perception. Learning happens at the boundary interactions between prior knowledge and incoming data. Separating complex mixtures of sound sources such as music requires a complex interplay between prior knowledge and analysis of incoming data. Many approaches to CASA can also be broadly categorized as either model-based or grouping-based. Although it is known that our perceptual-system utilizes both of these types of processing, building such systems computationally has been challenging. As a result most existing systems either rely on prior source models or are solely based on grouping cues. In this chapter the authors argue that formulating this integration problem as clustering based on similarities between time-frequency atoms provides an expressive yet disciplined approach to building sound source characterization and separation systems and evaluating their performance. After describing the main components of such an architecture, the authors describe a concrete realization that is based on spectral clustering of a sinusoidal representation. They show how this approach can be used to model both traditional grouping cues such as frequency and amplitude continuity as well as other types of information and prior knowledge such as onsets, harmonicity and timbre-models for specific instruments. Experiments supporting their approach to integration are also described. The description also covers issues of software architecture, implementation and efficiency, which are frequently not analyzed in depth for many existing algorithms. The resulting system exhibits practical performance (approximately real-time) with consistent results without requiring example-specific parameter optimization and is available as part of the Marsyas open source audio processing framework.

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Joint localization and fingerprinting of sound sources for auditory scene analysis

2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings ◽

10.1109/rose.2014.6952982 ◽

2014 ◽

Author(s):

Scott Kaghaz-Garan ◽

Anurag Umbarkar ◽

Alex Doboli

Keyword(s):

Auditory Scene Analysis ◽

Scene Analysis ◽

Sound Sources ◽

Auditory Scene

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Auditory Scene Analysis: An Attention Perspective

Journal of Speech Language and Hearing Research ◽

10.1044/2017_jslhr-h-17-0041 ◽

2017 ◽

Vol 60 (10) ◽

pp. 2989-3000 ◽

Cited By ~ 18

Author(s):

Elyse S. Sussman

Keyword(s):

Neural Model ◽

Auditory Scene Analysis ◽

Stream Segregation ◽

Scene Analysis ◽

Sound Sources ◽

Auditory Scene ◽

System Data ◽

New Perspective ◽

Reported Data ◽

Task Goals

Purpose This review article provides a new perspective on the role of attention in auditory scene analysis. Method A framework for understanding how attention interacts with stimulus-driven processes to facilitate task goals is presented. Previously reported data obtained through behavioral and electrophysiological measures in adults with normal hearing are summarized to demonstrate attention effects on auditory perception—from passive processes that organize unattended input to attention effects that act at different levels of the system. Data will show that attention can sharpen stream organization toward behavioral goals, identify auditory events obscured by noise, and limit passive processing capacity. Conclusions A model of attention is provided that illustrates how the auditory system performs multilevel analyses that involve interactions between stimulus-driven input and top-down processes. Overall, these studies show that (a) stream segregation occurs automatically and sets the basis for auditory event formation; (b) attention interacts with automatic processing to facilitate task goals; and (c) information about unattended sounds is not lost when selecting one organization over another. Our results support a neural model that allows multiple sound organizations to be held in memory and accessed simultaneously through a balance of automatic and task-specific processes, allowing flexibility for navigating noisy environments with competing sound sources. Presentation Video http://cred.pubs.asha.org/article.aspx?articleid=2601618

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Progress in Understanding Auditory Scene Analysis

Music Perception An Interdisciplinary Journal ◽

10.1525/mp.2015.33.1.12 ◽

2015 ◽

Vol 33 (1) ◽

pp. 12-19 ◽

Cited By ~ 9

Author(s):

Albert S. Bregman

Keyword(s):

Subjective Experience ◽

Newborn Infants ◽

Auditory Scene Analysis ◽

Scene Analysis ◽

Computational Auditory Scene Analysis ◽

Musical Style ◽

Sound Sources ◽

Auditory Scene ◽

Productive Research ◽

Difficult Challenge

In this paper, I make the following claims: (1) Subjective experience is tremendously useful in guiding productive research. (2) Studies of auditory scene analysis (ASA) in adults, newborn infants, and non-human animals (e.g., in goldfish or pigeons) establish the generality of ASA and suggest that it has an innate foundation. (3) ASA theory does not favor one musical style over another. (4) The principles used in the composition of polyphony (slightly modified) apply not only to one particular musical style or culture but to any form of layered music. (5) Neural explanations of ASA do not supersede explanations in terms of capacities; the two are complementary. (6) In computational auditory scene analysis (CASA) – ASA by computer systems – or any adequate theory of ASA, the most difficult challenge will be to discover how the contributions of a very large number of types of acoustical evidence and top-down schemas (acquired knowledge about the sound sources in our environments), can be coordinated without producing conflict that disables the system. (7) Finally I argue that the movement of a listener within the auditory scene provides him/her/it with rich information that should not be ignored by ASA theorists and researchers.

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