An Industrial-Strength Audio Search Algorithm.¶
Wang, A. L. (2003). An Industrial-Strength Audio Search Algorithm. Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR), 7-13.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Hashing
- In forensics and identity it is biometric templates, perceptual image hashing for duplicate detection, and audio fingerprinting.
This sourceThe Shazam audio-fingerprinting algorithm — a combinatorially hashed time-frequency fingerprint used as a noise-robust handle for matching audio against a large database.
Supported in partVerified against the work's full text
Wang's Shazam paper supports audio fingerprinting as a hashed-token matching method but not biometric templates or perceptual image hashing for duplicate detection.
“Each audio file is “fingerprinted,” a process in which reproducible hash tokens are extracted.”
- In forensics and identity it is biometric templates, perceptual image hashing for duplicate detection, and audio fingerprinting.
Mechanisms¶
- Digital Watermark or Content Fingerprint
- … is that identity travels with the content through the copy chain, needing no cooperating metadata and no chain-of-custody — a fingerprint of the content's own perceptual features re-identifies a track even after re-encoding, the same principle behind large-scale acoustic fingerprinting and content-matching systems.
This sourceUses reproducible acoustic fingerprints derived from audio content to identify tracks from degraded, noisy, or codec-compressed samples at large database scale.
- … is that identity travels with the content through the copy chain, needing no cooperating metadata and no chain-of-custody — a fingerprint of the content's own perceptual features re-identifies a track even after re-encoding, the same principle behind large-scale acoustic fingerprinting and content-matching systems.
Verification¶
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