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계열Machine learningMachine learning
기원 연도20022001
창시자Jeroen HaitsmaBeth Logan
유형Perceptual audio hashingContent-based audio similarity
원전Haitsma, J., & Kalker, T. (2002). A highly robust audio fingerprinting system. In Proceedings of the International Symposium on Music Information Retrieval. link ↗Logan, B., & Salomon, A. (2001). A music similarity function based on song structure. In Proceedings of the International Conference on Music Information Retrieval. link ↗
별칭robust hashing, perceptual hashing, music identificationmusic distance metric, timbral similarity, content-based similarity
관련55
요약Audio fingerprinting is a technique for creating a compact, robust identifier (fingerprint) for audio recordings that uniquely represents the content while being tolerant to modifications such as compression, noise, or time-shifting. Introduced by Haitsma and Kalker (2002), it underlies music identification services like Shazam and is critical for copyright enforcement, music matching, and library deduplication. A fingerprint is not a waveform hash; it captures perceptual content and remains stable across reasonable audio alterations.Music similarity measures are computational methods for assessing how musically related two audio recordings are. Introduced by Logan (2001), similarity measures enable content-based music recommendation, playlist generation, and music discovery. Unlike fingerprinting, which identifies the same song, similarity measures gauge stylistic, timbral, and structural resemblance between different songs. Measures can be acoustic (comparing spectral features), high-level (genre, mood), or hybrid.
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ScholarGate방법 비교: Audio Fingerprinting · Music Similarity Measure. 2026-06-20에 다음에서 검색함: https://scholargate.app/ko/compare