Moira Kelly Boloyos, Thea Kaylee Libunao, Jerome Masilungan, Franz A. de Leon, C. R. Lucas, Carl Timothy Tolentino
{"title":"Monophonic Audio-Based Automatic Acoustic Guitar Tablature Transcription System with Legato Identification","authors":"Moira Kelly Boloyos, Thea Kaylee Libunao, Jerome Masilungan, Franz A. de Leon, C. R. Lucas, Carl Timothy Tolentino","doi":"10.1109/TENCON54134.2021.9707430","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":405859,"journal":{"name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON54134.2021.9707430","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.