Exploring the distribution of statistical feature parameters for natural sound textures.

Sounds like "running water" and "buzzing bees" are classes of sounds which are a collective result of many similar acoustic events and are known as "sound textures". A recent psychoacoustic study using sound textures has reported that natural sounding textures can be sy...

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Autores principales:	Ambika P Mishra, Nicol S Harper, Jan W H Schnupp
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fc678df203b14a37b2c70a2bda0c40c7
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id	oai:doaj.org-article:fc678df203b14a37b2c70a2bda0c40c7
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spelling	oai:doaj.org-article:fc678df203b14a37b2c70a2bda0c40c72021-12-02T20:10:15ZExploring the distribution of statistical feature parameters for natural sound textures.1932-620310.1371/journal.pone.0238960https://doaj.org/article/fc678df203b14a37b2c70a2bda0c40c72021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0238960https://doaj.org/toc/1932-6203Sounds like "running water" and "buzzing bees" are classes of sounds which are a collective result of many similar acoustic events and are known as "sound textures". A recent psychoacoustic study using sound textures has reported that natural sounding textures can be synthesized from white noise by imposing statistical features such as marginals and correlations computed from the outputs of cochlear models responding to the textures. The outputs being the envelopes of bandpass filter responses, the 'cochlear envelope'. This suggests that the perceptual qualities of many natural sounds derive directly from such statistical features, and raises the question of how these statistical features are distributed in the acoustic environment. To address this question, we collected a corpus of 200 sound textures from public online sources and analyzed the distributions of the textures' marginal statistics (mean, variance, skew, and kurtosis), cross-frequency correlations and modulation power statistics. A principal component analysis of these parameters revealed a great deal of redundancy in the texture parameters. For example, just two marginal principal components, which can be thought of as measuring the sparseness or burstiness of a texture, capture as much as 64% of the variance of the 128 dimensional marginal parameter space, while the first two principal components of cochlear correlations capture as much as 88% of the variance in the 496 correlation parameters. Knowledge of the statistical distributions documented here may help guide the choice of acoustic stimuli with high ecological validity in future research.Ambika P MishraNicol S HarperJan W H SchnuppPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0238960 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ambika P Mishra Nicol S Harper Jan W H Schnupp Exploring the distribution of statistical feature parameters for natural sound textures.
description	Sounds like "running water" and "buzzing bees" are classes of sounds which are a collective result of many similar acoustic events and are known as "sound textures". A recent psychoacoustic study using sound textures has reported that natural sounding textures can be synthesized from white noise by imposing statistical features such as marginals and correlations computed from the outputs of cochlear models responding to the textures. The outputs being the envelopes of bandpass filter responses, the 'cochlear envelope'. This suggests that the perceptual qualities of many natural sounds derive directly from such statistical features, and raises the question of how these statistical features are distributed in the acoustic environment. To address this question, we collected a corpus of 200 sound textures from public online sources and analyzed the distributions of the textures' marginal statistics (mean, variance, skew, and kurtosis), cross-frequency correlations and modulation power statistics. A principal component analysis of these parameters revealed a great deal of redundancy in the texture parameters. For example, just two marginal principal components, which can be thought of as measuring the sparseness or burstiness of a texture, capture as much as 64% of the variance of the 128 dimensional marginal parameter space, while the first two principal components of cochlear correlations capture as much as 88% of the variance in the 496 correlation parameters. Knowledge of the statistical distributions documented here may help guide the choice of acoustic stimuli with high ecological validity in future research.
format	article
author	Ambika P Mishra Nicol S Harper Jan W H Schnupp
author_facet	Ambika P Mishra Nicol S Harper Jan W H Schnupp
author_sort	Ambika P Mishra
title	Exploring the distribution of statistical feature parameters for natural sound textures.
title_short	Exploring the distribution of statistical feature parameters for natural sound textures.
title_full	Exploring the distribution of statistical feature parameters for natural sound textures.
title_fullStr	Exploring the distribution of statistical feature parameters for natural sound textures.
title_full_unstemmed	Exploring the distribution of statistical feature parameters for natural sound textures.
title_sort	exploring the distribution of statistical feature parameters for natural sound textures.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/fc678df203b14a37b2c70a2bda0c40c7
work_keys_str_mv	AT ambikapmishra exploringthedistributionofstatisticalfeatureparametersfornaturalsoundtextures AT nicolsharper exploringthedistributionofstatisticalfeatureparametersfornaturalsoundtextures AT janwhschnupp exploringthedistributionofstatisticalfeatureparametersfornaturalsoundtextures
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Exploring the distribution of statistical feature parameters for natural sound textures.

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