Towards End-to-end Audio-sheet-music Retrieval

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keydorfer2016towards

Related papers

• Towards Robust And Truly Large-scale Audio-sheet Music Retrieval (2023)4.52
• Self-supervised Contrastive Learning For Robust Audio-sheet Music Retrieval Systems (2023)5.24
• Cross-modal Music Retrieval And Applications: An Overview Of Key Methodologies (2019)12.68
• Passage Summarization With Recurrent Models For Audio-sheet Music Retrieval (2023)0.00
• Audio-visual Embedding For Cross-modal Musicvideo Retrieval Through Supervised Deep CCA (2019)11.93
• Musictm-dataset For Joint Representation Learning Among Sheet Music, Lyrics, And Musical Audio (2020)3.58
• Learning Soft-attention Models For Tempo-invariant Audio-sheet Music Retrieval (2019)0.00
• Deep Cross-modal Correlation Learning For Audio And Lyrics In Music Retrieval (2017)14.06