# Audio Fingerprinting with Holographic Reduced Representations

arXiv: https://arxiv.org/abs/2406.13139 · HTML: https://mlsanity.com/paper/2406.13139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2024.

## Built on by (most cited first, 2 of 2)

- [Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance](https://mlsanity.com/paper/2601.17690/) (2026)
- [Learning Holographic Reduced Representations with Clifford Variational Autoencoders](https://mlsanity.com/paper/2609.28409/) (2026)

## Abstract (arXiv)

This paper proposes an audio fingerprinting model with holographic reduced representation (HRR). The proposed method reduces the number of stored fingerprints, whereas conventional neural audio fingerprinting requires many fingerprints for each audio track to achieve high accuracy and time resolution. We utilize HRR to aggregate multiple fingerprints into a composite fingerprint via circular convolution and summation, resulting in fewer fingerprints with the same dimensional space as the original. Our search method efficiently finds a combined fingerprint in which a query fingerprint exists. Using HRR's inverse operation, it can recover the relative position within a combined fingerprint, retaining the original time resolution. Experiments show that our method can reduce the number of fingerprints with modest accuracy degradation while maintaining the time resolution, outperforming simple decimation and summation-based aggregation methods.