# Pretrained Conformers for Audio Fingerprinting and Retrieval

arXiv: https://arxiv.org/abs/2508.11609 · HTML: https://mlsanity.com/paper/2508.11609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [A scalable noisy speech dataset and online subjective test framework](https://mlsanity.com/paper/1909.08050/) (2019)
- [Now Playing: Continuous low-power music recognition](https://mlsanity.com/paper/1711.10958/) (2017)
- [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)
- [Simultaneously Learning Robust Audio Embeddings and balanced Hash codes for Query-by-Example](https://mlsanity.com/paper/2211.11060/) (2022)
- [Knowledge Distillation for Real-Time Classification of Early Media in Voice Communications](https://mlsanity.com/paper/2410.21478/) (2024)

## Abstract (arXiv)

Conformers have shown great results in speech processing due to their ability to capture both local and global interactions. In this work, we utilize a self-supervised contrastive learning framework to train conformer-based encoders that are capable of generating unique embeddings for small segments of audio, generalizing well to previously unseen data. We achieve state-of-the-art results for audio retrieval tasks while using only 3 seconds of audio to generate embeddings. Our models are almost completely immune to temporal misalignments and achieve state-of-the-art results in cases of other audio distortions such as noise, reverb or extreme temporal stretching. Code and models are made publicly available and the results are easy to reproduce as we train and test using popular and freely available datasets of different sizes.