# Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance

arXiv: https://arxiv.org/abs/2601.17690 · HTML: https://mlsanity.com/paper/2601.17690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 25 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Streaming keyword spotting on mobile devices](https://mlsanity.com/paper/2005.06720/) (2020)
- [SHAS: Approaching optimal Segmentation for End-to-End Speech Translation](https://mlsanity.com/paper/2202.04774/) (2022)
- [Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning](https://mlsanity.com/paper/2010.11910/) (2020)
- [Attention-Based Audio Embeddings for Query-by-Example](https://mlsanity.com/paper/2210.08624/) (2022)
- [Speaker Targeting via Self-Speaker Adaptation for Multi-talker ASR](https://mlsanity.com/paper/2506.22646/) (2025)
- [GraFPrint: A GNN-Based Approach for Audio Identification](https://mlsanity.com/paper/2410.10994/) (2024)
- [Mamba-based Segmentation Model for Speaker Diarization](https://mlsanity.com/paper/2410.06459/) (2024)
- [Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models](https://mlsanity.com/paper/2505.17446/) (2025)
- [Enhancing Neural Audio Fingerprint Robustness to Audio Degradation for Music Identification](https://mlsanity.com/paper/2506.22661/) (2025)
- [How Much Context Does My Attention-Based ASR System Need?](https://mlsanity.com/paper/2310.15672/) (2023)
- [Audio Fingerprinting with Holographic Reduced Representations](https://mlsanity.com/paper/2406.13139/) (2024)
- [Lightweight Audio Segmentation for Long-form Speech Translation](https://mlsanity.com/paper/2406.10549/) (2024)

## Built on by (most cited first, 1 of 1)

- [Turning music identification into a neural forward pass](https://mlsanity.com/paper/2606.17301/) (2026)

## Abstract (arXiv)

Audio fingerprinting provides an identifiable representation of acoustic signals, which can be later used for identification and retrieval systems. To obtain a discriminative representation, the input audio is usually segmented into shorter time intervals, allowing local acoustic features to be extracted and analyzed. Modern neural approaches typically operate on short, fixed-duration audio segments, yet the choice of segment duration is often made heuristically and rarely examined in depth. In this paper, we study how segment length affects audio fingerprinting performance. We extend an existing neural fingerprinting architecture to adopt various segment lengths and evaluate retrieval accuracy across different segment lengths and query durations. Our results show that short segment lengths (0.5-second) generally achieve better performance. Moreover, we evaluate LLM capacity in recommending the best segment length, which shows that GPT-5-mini consistently gives the best suggestions across five considerations among three studied LLMs. Our findings provide practical guidance for selecting segment duration in large-scale neural audio retrieval systems.