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| Tempo-Schätzung× | Musik-Ähnlichkeitsmaße× | |
|---|---|---|
| Fachgebiet | Music Information Retrieval | Music Information Retrieval |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1998 | 2001 |
| Urheber≠ | Eric D. Scheirer | Beth Logan |
| Typ≠ | Audio tempo analysis | Content-based audio similarity |
| Wegweisende Quelle≠ | Scheirer, E. D. (1998). Tempo and beat analysis of acoustic musical signals. The Journal of the Acoustical Society of America, 103(1), 588-601. DOI ↗ | Logan, B., & Salomon, A. (2001). A music similarity function based on song structure. In Proceedings of the International Conference on Music Information Retrieval. link ↗ |
| Aliasnamen | tempo detection, BPM estimation, pulse rate detection | music distance metric, timbral similarity, content-based similarity |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Tempo estimation is the task of automatically determining the beats per minute (BPM) or tempo of a musical recording. Introduced by Scheirer (1998), it is fundamental to rhythm analysis, music classification, and synchronization applications. Tempo is one of the most perceptually salient features of music; accurate estimation enables music-aware systems and human-machine interaction. Unlike beat tracking, which produces discrete beat times, tempo estimation yields a single BPM value (or a distribution of likely tempi). | 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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