Symbolic & Acoustic: Multi-domain Music Emotion Modeling for Instrumental Music

TL;DR

This paper introduces a multi-domain music emotion recognition method combining symbolic and acoustic analysis, improving accuracy and interpretability in instrumental music emotion modeling, especially with limited data.

Contribution

It presents a novel multi-domain approach that integrates symbolic and acoustic features for more accurate and interpretable music emotion recognition.

Findings

Achieved a 2.4% increase in overall accuracy over baseline

Established state-of-the-art performance on EMOPIA dataset

Effectively utilizes limited labeled data for emotion modeling

Abstract

Music Emotion Recognition involves the automatic identification of emotional elements within music tracks, and it has garnered significant attention due to its broad applicability in the field of Music Information Retrieval. It can also be used as the upstream task of many other human-related tasks such as emotional music generation and music recommendation. Due to existing psychology research, music emotion is determined by multiple factors such as the Timbre, Velocity, and Structure of the music. Incorporating multiple factors in MER helps achieve more interpretable and finer-grained methods. However, most prior works were uni-domain and showed weak consistency between arousal modeling performance and valence modeling performance. Based on this background, we designed a multi-domain emotion modeling method for instrumental music that combines symbolic analysis and acoustic analysis. At the same time, because of the rarity of music data and the difficulty of labeling, our multi-domain approach can make full use of limited data. Our approach was implemented and assessed using the publicly available piano dataset EMOPIA, resulting in a notable improvement over our baseline model with a 2.4% increase in overall accuracy, establishing its state-of-the-art performance.