CITATION — REFERENCE ENTRY
Learning Audio–Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification — Transactions of the International Society for Music Information Retrieval
- Key
- dorfer2018-audio-sheet-music
- Authors
- Dorfer, Matthias; Hajič jr., Jan; Arzt, Andreas; Frostel, Harald; Widmer, Gerhard
- Issued
- 2018-9-4
- Type
- article-journal
- Container
- Transactions of the International Society for Music Information Retrieval
- Volume
- 1
- Issue
- 1
- Pages
- 22-33
Raw CSL JSON
{
"DOI": "10.5334/tismir.12",
"URL": "https://transactions.ismir.net/articles/10.5334/tismir.12",
"page": "22-33",
"type": "article-journal",
"issue": "1",
"title": "Learning Audio–Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification",
"author": [
{
"given": "Matthias",
"family": "Dorfer"
},
{
"given": "Jan",
"family": "Hajič jr."
},
{
"given": "Andreas",
"family": "Arzt"
},
{
"given": "Harald",
"family": "Frostel"
},
{
"given": "Gerhard",
"family": "Widmer"
}
],
"issued": {
"date-parts": [
[
2018,
9,
4
]
]
},
"volume": "1",
"container-title": "Transactions of the International Society for Music Information Retrieval"
}
Claims
-
Multimodal convolutional networks learn joint embeddings of short audio excerpts and sheet-music images, supporting piece identification from audio and retrieval of performances from a score.
"We propose a method that learns joint embedding spaces for short excerpts of audio and their respective counterparts in sheet music images, using multimodal convolutional neural networks. Given the learned representations, we show how to utilize them for two sheet-music-related tasks: (1) piece/score identification from audio queries and (2) retrieving relevant performances given a score as a search query."
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