# Topological fingerprints for audio identification

arXiv: https://arxiv.org/abs/2309.03516 · HTML: https://mlsanity.com/paper/2309.03516/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2023.

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

- [Topology-enhanced machine learning for speech signal processing](https://mlsanity.com/paper/2311.15210/) (2023)
- [The Morse Transform for Discrete Shape Analysis](https://mlsanity.com/paper/2503.04507/) (2025)

## Abstract (arXiv)

We present a topological audio fingerprinting approach for robustly identifying duplicate audio tracks. Our method applies persistent homology on local spectral decompositions of audio signals, using filtered cubical complexes computed from mel-spectrograms. By encoding the audio content in terms of local Betti curves, our topological audio fingerprints enable accurate detection of time-aligned audio matchings. Experimental results demonstrate the accuracy of our algorithm in the detection of tracks with the same audio content, even when subjected to various obfuscations. Our approach outperforms existing methods in scenarios involving topological distortions, such as time stretching and pitch shifting.