研究音频指纹的片段长度如何影响识别效果，发现0.5秒最优。
Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance
- 测试不同片段长度对音频指纹性能的影响，扩展神经架构支持多段长。
- 0.5秒片段在检索准确率上表现最佳，优于更长或更短的片段。
- 用大模型推荐片段长度，GPT-5-mini表现最稳定可靠。
音频指纹为声学信号提供可识别的表示，可用于后续的识别与检索系统。为获得具有区分性的表示，输入音频通常被分割为较短的时间片段，以便提取和分析局部声学特征。现代神经方法通常处理固定时长的短片段，但片段时长的选择常凭经验决定，缺乏深入研究。本文研究片段长度对音频指纹性能的影响，扩展现有神经指纹架构以支持多种片段长度，并在不同片段长度与查询时长下评估检索准确率。结果表明，短片段（0.5秒）通常表现更好。此外，我们评估了大语言模型在推荐最优片段长度方面的能力，结果显示在三个受试模型中，GPT-5-mini在五项评估维度上始终给出最佳建议。研究为大规模神经音频检索系统的片段时长选择提供了实用指导。
原文摘要 · Abstract (English)
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.
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