Bandingkan kaedah

	Segmentasi Muzik	Penjejakan Rentak	Pengecaman Kord	Pengekstrakan Melodi	Klasifikasi Genre Muzik
Bidang	Capaian Maklumat Muzik	Capaian Maklumat Muzik	Capaian Maklumat Muzik	Capaian Maklumat Muzik	Capaian Maklumat Muzik
Keluarga	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
Tahun asal	2001	2007	2005	2008	2002
Pengasas	Masataka Goto	David P. Ellis	Christopher Harte	Anssi Klapuri	George Tzanetakis
Jenis	Audio structural analysis	Audio signal processing algorithm	Harmonic audio analysis	Polyphonic audio analysis	Audio feature-based classification
Sumber perintis	Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval.	Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60.	Harte, C., Sandler, M. B., Abdallah, S. A., & Gómez, E. (2005). Symbolic representation of musical chords: Proposed extensions to the HarmO ontology. In Proceedings of the International Society for Music Information Retrieval Conference.	Salamon, J., & Gómez, E. (2014). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 1759-1770.	Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302.
Alias	structural segmentation, music structure analysis, section boundary detection	pulse detection, beat detection, metrical analysis	chord estimation, harmonic analysis, chord detection	pitch contour extraction, melodic line extraction, f0 tracking	genre recognition, music categorization, style classification
Berkaitan	5	5	5	5	5
Ringkasan	Music segmentation is the task of dividing a musical recording into distinct structural sections (e.g., verse, chorus, bridge, pre-chorus, outro). Introduced by Goto (2001), it identifies major structural boundaries and labels sections according to musical form. Segmentation is essential for music understanding, audio editing, and composition analysis. It enables higher-level tasks like cover song identification and song structure-aware music generation.	Beat tracking is an algorithm for automatically identifying the temporal positions of musical beats in audio recordings. It has been widely studied since the early 2000s, particularly for rhythm analysis and music synchronization applications. The problem is central to music information retrieval and essential for music-aware systems.	Chord recognition is the task of automatically identifying the harmonic chords present in a musical recording and estimating when chord changes occur. Introduced formally by Harte et al. (2005), it is a cornerstone of music analysis and widely used in music education, cover song analysis, and musical structure understanding. Modern systems use deep learning to classify and sequence chords in real time.	Melody extraction is the task of automatically isolating the main melodic contour from polyphonic music recordings. It originated from music transcription research in the 2000s and addresses the core challenge of human pitch perception: identifying the perceptually dominant pitch when many instruments play simultaneously. Modern approaches use deep learning and are essential for music analysis, cover song detection, and music-to-lyrics alignment.	Music genre classification is the task of automatically assigning genre labels (rock, jazz, classical, pop, etc.) to audio recordings. Introduced formally by Tzanetakis and Cook (2002), it is one of the earliest and most studied music information retrieval problems. It remains critical for music discovery, recommendation systems, digital library organization, and music streaming services. Modern systems achieve high accuracy on standard datasets using deep learning.