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Modelling Timbral Hardness

APPLIED SCIENCES-BASEL(2019)

Cited 4|Views19
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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.
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Key words
audio coding,artificial intelligence,sound recording,sound quality,psychoacoustics,timbre,modelling,perception,music information retrieval
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