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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.17690 |
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| _version_ | 1866914277851398144 |
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| author | Gong, Ziling Ouyang, Yunyan Kamdar, Iram Ma, Melody Chen, Hongjie Dernoncourt, Franck Rossi, Ryan A. Ahmed, Nesreen K. |
| author_facet | Gong, Ziling Ouyang, Yunyan Kamdar, Iram Ma, Melody Chen, Hongjie Dernoncourt, Franck Rossi, Ryan A. Ahmed, Nesreen K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17690 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance Gong, Ziling Ouyang, Yunyan Kamdar, Iram Ma, Melody Chen, Hongjie Dernoncourt, Franck Rossi, Ryan A. Ahmed, Nesreen K. Sound Artificial Intelligence Information Retrieval Machine Learning Audio and Speech Processing 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. |
| title | Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance |
| topic | Sound Artificial Intelligence Information Retrieval Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.17690 |