Automatic Music Transcription An overview

被引：114

作者：

Benetos, Emmanouil ^{[1
,2
,3
]}

Dixon, Simon ^{[1
,4
]}

Duan, Zhiyao ^{[5
]}

Ewert, Sebastian ^{[6
]}

机构：

[1] Queen Mary Univ London, Ctr Digital Mus, London, England

[2] Alan Turing Inst, London, England

[3] City Univ London, Dept Comp Sci, London, England

[4] ISMIR, London, England

[5] Univ Rochester, Elect & Comp Engn Dept, Rochester, NY USA

[6] Spotify, Luxembourg, Luxembourg

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2019年 / 36卷 / 01期

关键词：

POLYPHONIC MUSIC; MULTIPITCH ESTIMATION;

D O I：

10.1109/MSP.2018.2869928

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The capability of transcribing music audio into music notation is a fascinating example of human intelligence. It involves perception (analyzing complex auditory scenes), cognition (recognizing musical objects), knowledge representation (forming musical structures), and inference (testing alternative hypotheses). Automatic music transcription (AMT), i.e., the design of computational algorithms to convert acoustic music signals into some form of music notation, is a challenging task in signal processing and artificial intelligence. It comprises several subtasks, including multipitch estimation (MPE), onset and offset detection, instrument recognition, beat and rhythm tracking, interpretation of expressive timing and dynamics, and score typesetting. © 1991-2012 IEEE.

引用

页码：20 / 30

页数：11

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