Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation
This work addresses the challenge of explaining expressive qualities in specialized acoustic domains like solo piano music for music information retrieval, though it is incremental as it builds on existing domain adaptation techniques.
The authors tackled the problem of poor generalization of emotion prediction models to solo piano music by using unsupervised domain adaptation with receptive-field regularized deep neural networks, resulting in significantly improved generalization and better prediction of expressive qualities in classical piano performances as perceived by humans.
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.