Modelling Timbral Hardness

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.

Keywords

• audio coding
• artificial intelligence
• sound recording
• sound quality
• psychoacoustics
• timbre
• modelling
• perception
• music information retrieval