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This research introduces VLAFP, the first deep learning model capable of processing audio signals of variable length while preserving temporal dynamics during segmentation. By overcoming the rigid fixed-length constraints of existing methods, it significantly improves performance in live audio identification and retrieval across distorted recordings. The advancement represents a notable step forward in robust acoustic signal processing within the broader field of machine learning.
Variable-Length Audio Fingerprinting
arXiv:2603.23947v2 Announce Type: replace-cross Abstract: Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their originals through similar fingerprints. Existing deep learning approaches rigidly fingerprint fixed-length audio segments, thereby neglecting temporal dynamics during segmentation. To address limitations due to this rigidity, we propose Variable-Length Audio FingerPrinting (VLAFP), a novel method that supports variable-length fingerprinting. To the best of our knowledge, VLAFP is the first deep audio fingerprinting model capable of processing audio of variable length, for both training and testing. Our experiments show that VLAFP outperforms existing state-of-the-arts in live audio identification and audio retrieval across three real-world datasets.
Abstract:Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their originals through similar fingerprints. Existing deep learning approaches rigidly fingerprint fixed-length audio segments, thereby neglecting temporal dynamics during segmentation. To address limitations due to this rigidity, we propose Variable-Length Audio FingerPrinting (VLAFP), a novel method that supports variable-length fingerprinting. To the best of our knowledge, VLAFP is the first deep audio fingerprinting model capable of processing audio of variable length, for both training and testing. Our experiments show that VLAFP outperforms existing state-of-the-arts in live audio identification and audio retrieval across three real-world datasets.
Sound (cs.SD); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
arXiv:2603.23947 [cs.SD]
(or arXiv:2603.23947v2 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2603.23947 Focus to learn more arXiv-issued DOI via DataCite
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