A Violin Music Transcriber for Personalized Learning

This paper presents a new version of our violin music transcriber to support personalized learning. The proposed method is designed to detect duo-pitch (two strings being bowed at the same time) from real-world violin audio signals recorded in a home environment. Our method uses a semitone band spectrogram, a signal spectral representation with direct musical relevance. We exploit constraints of violin sound to improve the transcription performance and speed in comparison with existing methods. We have carried out rigorous evaluations using (a) single pitch notes and duo-phonic pitch samples within the violin´s playing range (G3-B6), and (b) music excerpts. For pitch and duo-pitch samples our method can achieve a transcription precision score of 93.1% and recall score of 96.7% respectively. For music excerpts, an average of 95% of all notes could be found (recall), and 93% of notes transcribed correctly (precision)