# Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies

arXiv: https://arxiv.org/abs/1902.04397 · HTML: https://mlsanity.com/paper/1902.04397/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [Contrastive Audio-Language Learning for Music](https://mlsanity.com/paper/2208.12208/) (2022)
- [PoseEmbroider: Towards a 3D, Visual, Semantic-aware Human Pose Representation](https://mlsanity.com/paper/2409.06535/) (2024)
- [Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems](https://mlsanity.com/paper/2309.12134/) (2023)
- [Towards Robust and Truly Large-Scale Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12158/) (2023)
- [Passage Summarization with Recurrent Models for Audio-Sheet Music Retrieval](https://mlsanity.com/paper/2309.12111/) (2023)
- [RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection](https://mlsanity.com/paper/2507.12175/) (2025)

## Abstract (arXiv)

There has been a rapid growth of digitally available music data, including audio recordings, digitized images of sheet music, album covers and liner notes, and video clips. This huge amount of data calls for retrieval strategies that allow users to explore large music collections in a convenient way. More precisely, there is a need for cross-modal retrieval algorithms that, given a query in one modality (e.g., a short audio excerpt), find corresponding information and entities in other modalities (e.g., the name of the piece and the sheet music). This goes beyond exact audio identification and subsequent retrieval of metainformation as performed by commercial applications like Shazam [1].