Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation

TL;DR

This paper presents a method using unsupervised domain adaptation and deep neural networks to improve the prediction and explanation of expressive qualities in solo piano recordings, addressing dataset limitations.

Contribution

It introduces a novel approach combining domain adaptation and receptive-field regularised neural networks to enhance generalisation to specialized acoustic domains.

Findings

Improved generalisation to solo piano music.

Enhanced prediction of expressive qualities.

Better alignment with human listener descriptions.

Abstract

Emotion and expressivity in music have been topics of considerable interest in the field of music information retrieval. In recent years, mid-level perceptual features have been suggested as means to explain computational predictions of musical emotion. We find that the diversity of musical styles and genres in the available dataset for learning these features is not sufficient for models to generalise well to specialised acoustic domains such as solo piano music. In this work, we show that by utilising unsupervised domain adaptation together with receptive-field regularised deep neural networks, it is possible to significantly improve generalisation to this domain. Additionally, we demonstrate that our domain-adapted models can better predict and explain expressive qualities in classical piano performances, as perceived and described by human listeners.