Automatic Assessment of Piano Performances Using Timbre and Pitch Features

Abstract

To assist piano learners with the improvement of their skills, this study investigates techniques for automatically assessing piano performances based on timbre and pitch features. The assessment is formulated as a classification problem that classifies piano performances as “Good”, “Fair”, or “Poor”. For timbre-based approaches, we propose timbre-based WaveNet, timbre-based MLNet, Timbre-based CNN, and Timbre-based CNN Transformers. For pitch-based approaches, we propose Pitch-based CNN and Pitch-based CNN Transformers. Our experiments indicate that both Pitch-based CNN and Pitch-based CNN Transformers are superior to the timbre-based approaches, which attained classification accuracies of 96.87% and 97.5%, respectively.

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