Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10390078" target="_blank" >RIV/00216208:11320/18:10390078 - isvavai.cz</a>

Result on the web

<a href="https://transactions.ismir.net/articles/10.5334/tismir.12/#" target="_blank" >https://transactions.ismir.net/articles/10.5334/tismir.12/#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5334/tismir.12" target="_blank" >10.5334/tismir.12</a>

Result language

angličtina

Original language name

Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification

Original language description

This work addresses the problem of matching musical audio directly to sheet music, without any higher-level abstract representation. 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. All retrieval models are trained and evaluated on a new, large scale multimodal audio-sheet music dataset which is made publicly available along with this article. The dataset comprises 479 precisely annotated solo piano pieces by 53 composers, for a total of 1,129 pages of music and about 15 hours of aligned audio, which was synthesized from these scores. Going beyond this synthetic training data, we carry out first retrieval experiments using scans of real sheet musi

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Transactions of the International Society for Music Information Retrieval

ISSN

2514-3298

e-ISSN

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Volume of the periodical

1

Issue of the periodical within the volume

1

Country of publishing house

CA - CANADA

Number of pages

12

Pages from-to

22-33

UT code for WoS article

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EID of the result in the Scopus database

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