Toward a High Performance Piano Practice Support System for Beginners
Shota Asahi, Satoshi Tamura, Yuko Sugiyama, Satoru Hayamizu · 2018
In piano learning, it is difficult especially for beginners to judge by themselves whether their musical performances are appropriate in terms of rhythm and melody. Therefore, we have been developing a piano practice support system, which enables piano beginners to conduct independent practice without their instructors. In this paper, we propose the system with the aid of a deep learning technique: Long Short-Term Memory (LSTM). Our system accepts raw piano sounds, extracting performance information. From these information, we evaluate performance. We evaluated the scheme using actual beginners' performances, and found the proposed system achieved better than previous conventional methods. This paper also presents an application employing our methods. Through subjective evaluation experiments for the proposed application, it turns out almost the all beginners found reflection points, and they maintained their motivation for independent practice.