Affiliation:
1. International Graduate Program of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan
2. Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
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.
Funder
Ministry of Science and Technology, Taiwan
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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