Сравнение методов

	Классификация музыкальных жанров	Сегментация музыки
Область	Извлечение музыкальной информации	Извлечение музыкальной информации
Семейство	Machine learning	Machine learning
Год появления	2002	2001
Автор метода	George Tzanetakis	Masataka Goto
Тип	Audio feature-based classification	Audio structural analysis
Основополагающий источник	Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302.	Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval.
Другие названия	genre recognition, music categorization, style classification	structural segmentation, music structure analysis, section boundary detection
Связанные	5	5
Сводка	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.	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.