Bowed string sequence estimation of a violin based on adaptive audio signal classification and context-dependent error correction

The sequence of strings played on a bowed string instrument is essential to understanding of the fingering. Thus, its estimation is required for machine understanding of violin playing. Audio-based identification is the only viable way to realize this goal for existing music recordings. A naïve implementation using audio classification alone, however, is inaccurate and is not robust against variations in string or instruments. We develop a bowed string sequence estimation method by combining audio-based bowed string classification and context-dependent error correction. The robustness against different setups of instruments improves by normalizing the F0-dependent features using the average feature of a recording. The performance of error correction is evaluated using an electric violin with two different brands of strings and and an acoustic violin. By incorporating mean normalization, the recognition error of recognition accuracy due to changing the string alleviates by 8 points, and that due to change of instrument by 12 points. Error correction decreases the error due to change of string by 8 points and that due to different instrument by 9 points.