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

Ziling Gong, Yunyan Ouyang, Iram Kamdar, Melody Ma, Hongjie Chen, Franck Dernoncourt, Ryan A. Rossi, Nesreen K. Ahmed

TL;DR

An existing neural fingerprinting architecture is extended to adopt various segment lengths and evaluate retrieval accuracy across different segment lengths and query durations to provide practical guidance for selecting segment duration in large-scale neural audio retrieval systems.

Abstract

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.

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

TL;DR

An existing neural fingerprinting architecture is extended to adopt various segment lengths and evaluate retrieval accuracy across different segment lengths and query durations to provide practical guidance for selecting segment duration in large-scale neural audio retrieval systems.

Abstract

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.
Paper Structure (8 sections, 2 equations, 3 figures, 3 tables)

This paper contains 8 sections, 2 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Experiments with NAFP and NAFP+
  3. Preliminary
  4. NAFP+ Design
  5. Evaluation
  6. Results
  7. Segment Length Selection with LLMs
  8. Conclusion

Figures (3)

  • Figure 1: Our motivation question: What is the optimal segment length for the audio fingerprinting task?
  • Figure 2: Hit Rate (%) of Various Segment Lengths $W$ (Green: $W=0.5$, Blue: $W=1$, Orange: $W=2$) and Query Lengths $L$.
  • Figure 3: Prompt Template: Fixed Context Prompt + One of the Five Question Prompts from Table \ref{['tab:llm_segment_length_b']}.