  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.
