Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance
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.
697c3a85a67238fac88cc1cc
Ziling Gong
697c3a85a67238fac88cc1cd
Yunyan Ouyang
697c3a85a67238fac88cc1ce
Iram Kamdar
697c3a85a67238fac88cc1cf
Melody Ma
697c3a85a67238fac88cc1d0
Hongjie Chen
697c3a85a67238fac88cc1d1
Franck Dernoncourt
claimed_verified
2026-01-30T09:35:35.306Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
Franck-Dernoncourt
user
Franck-Dernoncourt
697c3a85a67238fac88cc1d2
Ryan A. Rossi
697c3a85a67238fac88cc1d3
Nesreen K. Ahmed
Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance
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.
Neural audio fingerprinting performance varies with segment length, with short segments (0.5-second) generally providing better retrieval accuracy, and large language models showing promise in recommending optimal segment durations.
audio fingerprinting neural fingerprinting architecture retrieval accuracy segment length audio segmentation large language models GPT-5-mini