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arXiv · 2407.04518

From Audio Encoders to Piano Judges: Benchmarking Performance Understanding for Solo Piano

Huan Zhang·Jinhua Liang·Simon Dixon
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Abstract

Our study investigates an approach for understanding musical performances through the lens of audio encoding models, focusing on the domain of solo Western classical piano music. Compared to composition-level attribute understanding such as key or genre, we identify a knowledge gap in performance-level music understanding, and address three critical tasks: expertise ranking, difficulty estimation, and piano technique detection, introducing a comprehensive Pianism-Labelling Dataset (PLD) for this purpose. We leverage pre-trained audio encoders, specifically Jukebox, Audio-MAE, MERT, and DAC, demonstrating varied capabilities in tackling downstream tasks, to explore whether domain-specific fine-tuning enhances capability in capturing performance nuances. Our best approach achieved 93.6\% accuracy in expertise ranking, 33.7\% in difficulty estimation, and 46.7\% in technique detection, with Audio-MAE as the overall most effective encoder. Finally, we conducted a case study on Chopin Piano Competition data using trained models for expertise ranking, which highlights the challenge of accurately assessing top-tier performances.

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Huan Zhang, Jinhua Liang, Simon Dixon. 2024-07-05. From Audio Encoders to Piano Judges: Benchmarking Performance Understanding for Solo Piano. https://arxiv.org/abs/2407.04518

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