Matthias Dorfer, Andreas Arzt, Gerhard Widmer,
"Towards End-to-End Audio-Sheet-Music Retrieval"
: NIPS 2016 End-to-end Learning for Speech and Audio Processing Workshop, 2016
Original Titel:
Towards End-to-End Audio-Sheet-Music Retrieval
Sprache des Titels:
Englisch
Original Buchtitel:
NIPS 2016 End-to-end Learning for Speech and Audio Processing Workshop
Original Kurzfassung:
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
Forschungs-