CITATION — REFERENCE ENTRY
Automatic Music Transcription: An Overview — IEEE Signal Processing Magazine
- Key
- benetos2019-amt-overview
- Authors
- Benetos, Emmanouil; Dixon, Simon; Duan, Zhiyao; Ewert, Sebastian
- Issued
- 2019-1
- Type
- article-journal
- Container
- IEEE Signal Processing Magazine
- Volume
- 36
- Issue
- 1
- Pages
- 20-30
Raw CSL JSON
{
"DOI": "10.1109/MSP.2018.2869928",
"page": "20-30",
"type": "article-journal",
"issue": "1",
"title": "Automatic Music Transcription: An Overview",
"author": [
{
"given": "Emmanouil",
"family": "Benetos"
},
{
"given": "Simon",
"family": "Dixon"
},
{
"given": "Zhiyao",
"family": "Duan"
},
{
"given": "Sebastian",
"family": "Ewert"
}
],
"issued": {
"date-parts": [
[
2019,
1
]
]
},
"volume": "36",
"container-title": "IEEE Signal Processing Magazine"
}
Claims
-
Automatic music transcription converts acoustic music signals into notation and comprises subtasks including multi-pitch estimation, onset/offset detection, instrument recognition, beat and rhythm tracking, expressive timing and dynamics, and score typesetting.
"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."
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