Algorithms and Applications of Automatic Music Transcription

• Item type: SIG Technical Reports(1)
• 公開日: 2022-06-10
• タイトル: Algorithms and Applications of Automatic Music Transcription
• タイトル / 言語: en
• タイトル: Algorithms and Applications of Automatic Music Transcription
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 招待講演
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Queen Mary University of London
• 著者所属(英): en
• 著者所属(英): Queen Mary University of London
• 著者名: Simon, Dixon
• 著者名(英): Simon, Dixon
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Automatic music transcription is the task of creating a score representation (for example in Western common music notation) from an audio recording or live audio stream. Although research on this topic spans almost 50 years, progress in the last few years has been quite remarkable. The field has moved from a situation where data was scarce, methods were ad hoc, and there were no standard methodologies or datasets for comparing competing approaches, to the current state where data-rich models are trained and tested on standard benchmark datasets. Various transcription tasks are addressed, such as transcription of a single or multiple simultaneous instruments, and transcription of the main melody or the bass line, the chords or the lyrics. After discussing some of the methods used in music transcription, I will give examples of how transcription can be used to analyse and understand human music-making via melodic patterns in jazz improvisation, expressive timing in classical and jazz performance, and intonation of harpsichord tuning in early classical music.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Automatic music transcription is the task of creating a score representation (for example inWestern common music notation) from an audio recording or live audio stream. Although research on this topic spans almost 50 years, progress in the last few years has been quite remarkable. The field has moved from a situation where data was scarce, methods were ad hoc, and there were no standard methodologies or datasets for comparing competing approaches, to the current state where data-rich models are trained and tested on standard benchmark datasets. Various transcription tasks are addressed, such as transcription of a single or multiple simultaneous instruments, and transcription of the main melody or the bass line, the chords or the lyrics. After discussing some of the methods used in music transcription, I will give examples of how transcription can be used to analyse and understand human music-making via melodic patterns in jazz improvisation, expressive timing in classical and jazz performance, and intonation of harpsichord tuning in early classical music.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10442647
• 書誌情報: 研究報告音声言語情報処理（SLP）; 巻 2022-SLP-142, 号 4, p. 1-1, 発行日 2022-06-10
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8663
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会