Towards End-to-End Audio-Sheet-Music Retrieval
Sound
2016-12-16 v1 Information Retrieval
Machine Learning
Abstract
This paper demonstrates the feasibility of learning to retrieve short snippets of sheet music (images) when given a short query excerpt of music (audio) -- and vice versa --, without any symbolic representation of music or scores. This would be highly useful in many content-based musical retrieval scenarios. Our approach is based on Deep Canonical Correlation Analysis (DCCA) and learns correlated latent spaces allowing for cross-modality retrieval in both directions. Initial experiments with relatively simple monophonic music show promising results.
Keywords
Cite
@article{arxiv.1612.05070,
title = {Towards End-to-End Audio-Sheet-Music Retrieval},
author = {Matthias Dorfer and Andreas Arzt and Gerhard Widmer},
journal= {arXiv preprint arXiv:1612.05070},
year = {2016}
}
Comments
In NIPS 2016 End-to-end Learning for Speech and Audio Processing Workshop, Barcelona, Spain