# Simultaneously Learning Robust Audio Embeddings and balanced Hash codes for Query-by-Example

arXiv: https://arxiv.org/abs/2211.11060 · HTML: https://mlsanity.com/paper/2211.11060/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning](https://mlsanity.com/paper/2010.11910/) (2020)
- [Now Playing: Continuous low-power music recognition](https://mlsanity.com/paper/1711.10958/) (2017)

## Built on by (most cited first, 3 of 3)

- [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)
- [Pretrained Conformers for Audio Fingerprinting and Retrieval](https://mlsanity.com/paper/2508.11609/) (2025)

## Abstract (arXiv)

Audio fingerprinting systems must efficiently and robustly identify query snippets in an extensive database. To this end, state-of-the-art systems use deep learning to generate compact audio fingerprints. These systems deploy indexing methods, which quantize fingerprints to hash codes in an unsupervised manner to expedite the search. However, these methods generate imbalanced hash codes, leading to their suboptimal performance. Therefore, we propose a self-supervised learning framework to compute fingerprints and balanced hash codes in an end-to-end manner to achieve both fast and accurate retrieval performance. We model hash codes as a balanced clustering process, which we regard as an instance of the optimal transport problem. Experimental results indicate that the proposed approach improves retrieval efficiency while preserving high accuracy, particularly at high distortion levels, compared to the competing methods. Moreover, our system is efficient and scalable in computational load and memory storage.