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2025
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Percussion and Instrumentation in Music Emotion Recognition: A Feature Engineering Approach
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Abstract
We propose a new set of features for audio-based Music Emotion Recognition (MER) that are related to percussion and individual instrument information. One limitation of current feature engineering approaches in MER is that they primarily focus on melodic elements. However, the percussive elements and instrumentation are also essential for conveying and recognizing emotions in music. Our approach leverages the Demucs framework for music source separation (which enables drum channel separation) and the MT3 framework for automatic music transcription and instrument recognition. Building on the results of these frameworks, we created a new set of features that primarily capture information about musical texture, rhythm, dynamics, expressivity, tone color, and musical form. To validate our work, we utilized the MERGE dataset, which comprises over 3000 30-second audio clips annotated with Russell's emotion quadrants. To evaluate the impact of the new features, we compared classification results with those obtained using current state-of-the-art features, demonstrating statistically significant improvements in F1 score (from 71.1% to 74.2%). Moreover, the novel features helped to reduce the confusion between quadrants 3 and 4 (a common difficulty in MER models). The most significant finding of the present study is the impact of separately analyzing the drum channel, whose features proved particularly relevant.
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