# GraFPrint: A GNN-Based Approach for Audio Identification

arXiv: https://arxiv.org/abs/2410.10994 · HTML: https://mlsanity.com/paper/2410.10994/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2024.

## Built on by (most cited first, 7 of 7)

- [Refining music sample identification with a self-supervised graph neural network](https://mlsanity.com/paper/2506.14684/) (2025)
- [Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance](https://mlsanity.com/paper/2601.17690/) (2026)
- [Pretrained Conformers for Audio Fingerprinting and Retrieval](https://mlsanity.com/paper/2508.11609/) (2025)
- [Robust Neural Audio Fingerprinting using Music Foundation Models](https://mlsanity.com/paper/2511.05399/) (2025)
- [Variable-Length Audio Fingerprinting](https://mlsanity.com/paper/2603.23947/) (2026)
- [Turning music identification into a neural forward pass](https://mlsanity.com/paper/2606.17301/) (2026)
- [POLARIS: Training-Free Audio Fingerprinting with Saliency-Based Landmarks and Delaunay Grouping](https://mlsanity.com/paper/2609.14820/) (2026)

## Abstract (arXiv)

This paper introduces GraFPrint, an audio identification framework that leverages the structural learning capabilities of Graph Neural Networks (GNNs) to create robust audio fingerprints. Our method constructs a k-nearest neighbor (k-NN) graph from time-frequency representations and applies max-relative graph convolutions to encode local and global information. The network is trained using a self-supervised contrastive approach, which enhances resilience to ambient distortions by optimizing feature representation. GraFPrint demonstrates superior performance on large-scale datasets at various levels of granularity, proving to be both lightweight and scalable, making it suitable for real-world applications with extensive reference databases.