# Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning

arXiv: https://arxiv.org/abs/2010.11910 · HTML: https://mlsanity.com/paper/2010.11910/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [Generation or Replication: Auscultating Audio Latent Diffusion Models](https://mlsanity.com/paper/2310.10604/) (2023)
- [Simultaneously Learning Robust Audio Embeddings and balanced Hash codes for Query-by-Example](https://mlsanity.com/paper/2211.11060/) (2022)
- [Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges](https://mlsanity.com/paper/2402.13957/) (2024)
- [Refining music sample identification with a self-supervised graph neural network](https://mlsanity.com/paper/2506.14684/) (2025)
- [Music Plagiarism Detection: Problem Formulation and a Segment-based Solution](https://mlsanity.com/paper/2601.21260/) (2026)
- [Audio Denoising for Robust Audio Fingerprinting](https://mlsanity.com/paper/2212.11277/) (2022)
- [Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol](https://mlsanity.com/paper/2507.06070/) (2025)
- [Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance](https://mlsanity.com/paper/2601.17690/) (2026)
- [Music Augmentation and Denoising For Peak-Based Audio Fingerprinting](https://mlsanity.com/paper/2310.13388/) (2023)
- [A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-By-Humming Task](https://mlsanity.com/paper/2312.01092/) (2023)
- [Robust Neural Audio Fingerprinting using Music Foundation Models](https://mlsanity.com/paper/2511.05399/) (2025)
- [Twenty-Five Years of MIR Research: Achievements, Practices, Evaluations, and Future Challenges](https://mlsanity.com/paper/2511.07205/) (2025)
- [Variable-Length Audio Fingerprinting](https://mlsanity.com/paper/2603.23947/) (2026)
- [Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation](https://mlsanity.com/paper/2606.00629/) (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)

Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas. These replicas can simulate the degrading effects on original audio signals by applying small time offsets and various types of distortions, such as background noise and room/microphone impulse responses. In the segment-level search task, where the conventional audio fingerprinting systems used to fail, our system using 10x smaller storage has shown promising results. Our code and dataset are available at urlhttps://mimbres.github.io/neural-audio-fp/.