title: Predicting the perception of performed dynamics in music audio with ensemble learning.
abstract: By varying the dynamics in a musical performance, the musician can convey structure and different expressions. Spectral properties of most musical instruments change in a complex way with the performed dynamics, but dedicated audio features for modeling the parameter are lacking. In this study, feature extraction methods were developed to capture relevant attributes related to spectral characteristics and spectral fluctuations, the latter through a sectional spectral flux. Previously, ground truths ratings of performed dynamics had been collected by asking listeners to rate how soft/loud the musicians played in a set of audio files. The ratings, averaged over subjects, were used to train three different machine learning models, using the audio features developed for the study as input. The highest result was produced from an ensemble of multilayer perceptrons with an R2of 0.84. This result seems to be close to the upper bound, given the estimated uncertainty of the ground truth data. The result is well above that of individual human listeners of the previous listening experiment, and on par with the performance achieved from the average rating of six listeners. Features were analyzed with a factorial design, which highlighted the importance of source separation in the feature extraction.
authors.affiliation: KTH Royal Institute of Technology, School of Computer Science and Communication, Speech, Music and Hearing, Stockholm, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Elowsson
authors.affiliation: KTH Royal Institute of Technology, School of Computer Science and Communication, Speech, Music and Hearing, Stockholm, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Friberg
mesh_terms: D000161:Acoustic Stimulation; D000162:Acoustics; D001307:Auditory Perception; D003198:Computer Simulation; D006801:Humans; D007600:Judgment; D008144:Loudness Perception; D000069550:Machine Learning; D008960:Models, Psychological; D009146:Music; D010507:Periodicity; D010898:Pitch Perception; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013997:Time Factors; D013998:Time Perception