# Refining music sample identification with a self-supervised graph neural network

arXiv: https://arxiv.org/abs/2506.14684 · HTML: https://mlsanity.com/paper/2506.14684/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Beat this! Accurate beat tracking without DBN postprocessing](https://mlsanity.com/paper/2407.21658/) (2024)
- [Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning](https://mlsanity.com/paper/2010.11910/) (2020)
- [ByteCover: Cover Song Identification via Multi-Loss Training](https://mlsanity.com/paper/2010.14022/) (2020)
- [Attention-Based Audio Embeddings for Query-by-Example](https://mlsanity.com/paper/2210.08624/) (2022)
- [GraFPrint: A GNN-Based Approach for Audio Identification](https://mlsanity.com/paper/2410.10994/) (2024)
- [ATGNN: Audio Tagging Graph Neural Network](https://mlsanity.com/paper/2311.01526/) (2023)
- [Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset](https://mlsanity.com/paper/2502.06364/) (2025)

## Built on by (most cited first, 2 of 2)

- [Automatic Music Sample Identification with Multi-Track Contrastive Learning](https://mlsanity.com/paper/2510.11507/) (2025)
- [Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation](https://mlsanity.com/paper/2606.00629/) (2026)

## Abstract (arXiv)

Automatic sample identification (ASID), the detection and identification of portions of audio recordings that have been reused in new musical works, is an essential but challenging task in the field of audio query-based retrieval. While a related task, audio fingerprinting, has made significant progress in accurately retrieving musical content under "real world" (noisy, reverberant) conditions, ASID systems struggle to identify samples that have undergone musical modifications. Thus, a system robust to common music production transformations such as time-stretching, pitch-shifting, effects processing, and underlying or overlaying music is an important open challenge. In this work, we propose a lightweight and scalable encoding architecture employing a Graph Neural Network within a contrastive learning framework. Our model uses only 9% of the trainable parameters compared to the current state-of-the-art system while achieving comparable performance, reaching a mean average precision (mAP) of 44.2%. To enhance retrieval quality, we introduce a two-stage approach consisting of an initial coarse similarity search for candidate selection, followed by a cross-attention classifier that rejects irrelevant matches and refines the ranking of retrieved candidates - an essential capability absent in prior models. In addition, because queries in real-world applications are often short in duration, we benchmark our system for short queries using new fine-grained annotations for the Sample100 dataset, which we publish as part of this work.