Texture features for the reproduction of the perceptual organization of sound

TL;DR

This paper introduces a novel sound analysis method that separates sounds into textures like tones, pulses, and noise, and shows these features correlate with human perceptual organization.

Contribution

The paper proposes a new technique to analyze sound textures and links these features to human perceptual dimensions, advancing understanding of sound categorization.

Findings

Energy-based tonality correlates with the first perceptual dimension.

Energy-based pulsality strongly correlates with the first perceptual dimension.

Energy-based noisiness moderately correlates with the second perceptual dimension.

Abstract

Human categorization of sound seems predominantly based on sound source properties. To estimate these source properties we propose a novel sound analysis method, which separates sound into different sonic textures: tones, pulses, and broadband noises. The audible presence of tones or pulses corresponds to more extended cochleagram patterns than can be expected on the basis of correlations introduced by the gammachirp filterbank alone. We design tract features to respond to these extended patterns, and use these to identify areas of the time-frequency plane as tonal, pulsal, and noisy. Where an area is marked as noisy if it is neither tonal nor pulsal. To investigate whether a similar separation indeed underlies human perceptual organization we introduce tract based descriptors: tonality, pulsality, and noisiness. These descriptors keep track of either the total energy or the cochleagram area marked as respectively tonal, pulsal, and noisy. Energy based tonality and pulsality is strongly correlated with the first perceptual dimension of human subjects, while energy based noisiness correlates moderately with the second perceptual dimension. We conclude that harmonic, impact and continuous process sounds can be largely separated with energy based tonality, pulsality and noisiness.