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

Moira Kelly Boloyos, Thea Kaylee Libunao, Jerome Masilungan, Franz A. de Leon, C. R. Lucas, Carl Timothy Tolentino
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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.
单声道音频为基础的自动原声吉他音阶转录系统与连奏识别
在音乐社区中,音乐抄写在学习和分享音乐作品知识方面发挥着重要作用。然而,对于吉他制表,大多数现有的转录系统未能纳入发音检测。在本研究中,开发了一种自动吉他转录(AGT)系统,该系统使用单音吉他录音作为输入,以检测和识别所演奏的弦-fret组合和发音(连奏)。对每个系统块的算法进行了选择和修改,以适应系统规格。结果表明,改进后的琴键块精度从78%提高到87%,发音块f值从59%提高到84%。AGT系统还与商业音乐转录应用程序进行了比较。虽然两者都在不同的数据集上进行了训练,但AGT系统的表现优于后者,该系统的弦琴弦精度为78.65%,发音精度为93.23%,而商业应用的弦琴弦精度为48.44%,发音精度为70.31%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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