Monophonic Audio-Based Automatic Acoustic Guitar Tablature Transcription System with Legato Identification

• Main Authors: Boloyos, Moira Kelly, Libunao, Thea Kaylee, Masilungan, Jerome, Leon, Franz de, Lucas, Crisron Rudolf, Tolentino, Carl Timothy
• Format: Conference Proceeding
• Language: English
• Subjects: acoustic guitar; articulation detection; Buildings; Conferences; digital signal processing; Merging; Music; music transcription; Signal processing algorithms; Training
• ISSN: 2159-3450
• DOI: 10.1109/TENCON54134.2021.9707430

Music transcription plays a significant role in the music community in terms of learning and sharing knowledge about musical pieces. However, for guitar tablatures, most existing transcription systems fail to incorporate articulation detection. In this study, an automatic guitar transcription (AGT) system, which uses a monophonic guitar recording as input to detect and identify the string-fret combinations and articulations (legato) played, was developed. Algorithms for each system block were chosen and modified to fit the system specifications. Results show that the modifications led to improvements in the string-fret block accuracy, from 78% to 87%, and the articulation block F-measure, from 59% to 84%. The AGT system was also compared with a commercial music transcription application. While both were trained on different data sets, the AGT system outperformed the latter, with the system having 78.65% string-fret accuracy and 93.23% articulation accuracy compared to the commercial application's 48.44% string-fret accuracy and 70.31% articulation accuracy.