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Appl. Sci. 2019, 9(3), 466; https://doi.org/10.3390/app9030466

Modelling Timbral Hardness

Institute of Sound Recording, Department of Music and Media, University of Surrey, Guildford, Surrey GU2 7XH, UK
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Received: 10 December 2018 / Revised: 15 January 2019 / Accepted: 24 January 2019 / Published: 30 January 2019
(This article belongs to the Special Issue Psychoacoustic Engineering and Applications)
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Abstract

Hardness is the most commonly searched timbral attribute within freesound.org, a commonly used online sound effect repository. A perceptual model of hardness was developed to enable the automatic generation of metadata to facilitate hardness-based filtering or sorting of search results. A training dataset was collected of 202 stimuli with 32 sound source types, and perceived hardness was assessed by a panel of listeners. A multilinear regression model was developed on six features: maximum bandwidth, attack centroid, midband level, percussive-to-harmonic ratio, onset strength, and log attack time. This model predicted the hardness of the training data with R 2 = 0.76. It predicted hardness within a new dataset with R 2 = 0.57, and predicted the rank order of individual sources perfectly, after accounting for the subjective variance of the ratings. Its performance exceeded that of human listeners. View Full-Text
Keywords: audio coding; artificial intelligence; sound recording; sound quality; psychoacoustics; timbre; modelling; perception; music information retrieval audio coding; artificial intelligence; sound recording; sound quality; psychoacoustics; timbre; modelling; perception; music information retrieval
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Pearce, A.; Brookes, T.; Mason, R. Modelling Timbral Hardness. Appl. Sci. 2019, 9, 466.

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