# Attention-Based Audio Embeddings for Query-by-Example

arXiv: https://arxiv.org/abs/2210.08624 · HTML: https://mlsanity.com/paper/2210.08624/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2022.

## Built on by (most cited first, 8 of 8)

- [Refining music sample identification with a self-supervised graph neural network](https://mlsanity.com/paper/2506.14684/) (2025)
- [BEST-STD: Bidirectional Mamba-Enhanced Speech Tokenization for Spoken Term Detection](https://mlsanity.com/paper/2411.14100/) (2024)
- [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)
- [Pretrained Conformers for Audio Fingerprinting and Retrieval](https://mlsanity.com/paper/2508.11609/) (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)

## Abstract (arXiv)

An ideal audio retrieval system efficiently and robustly recognizes a short query snippet from an extensive database. However, the performance of well-known audio fingerprinting systems falls short at high signal distortion levels. This paper presents an audio retrieval system that generates noise and reverberation robust audio fingerprints using the contrastive learning framework. Using these fingerprints, the method performs a comprehensive search to identify the query audio and precisely estimate its timestamp in the reference audio. Our framework involves training a CNN to maximize the similarity between pairs of embeddings extracted from clean audio and its corresponding distorted and time-shifted version. We employ a channel-wise spectral-temporal attention mechanism to better discriminate the audio by giving more weight to the salient spectral-temporal patches in the signal. Experimental results indicate that our system is efficient in computation and memory usage while being more accurate, particularly at higher distortion levels, than competing state-of-the-art systems and scalable to a larger database.