Bandingkan kaedah

	Pengecaman Instrumen	Klasifikasi Genre Muzik
Bidang	Capaian Maklumat Muzik	Capaian Maklumat Muzik
Keluarga	Machine learning	Machine learning
Tahun asal	2005	2002
Pengasas	Antti Eronen	George Tzanetakis
Jenis	Timbre-based audio classification	Audio feature-based classification
Sumber perintis	Eronen, A., Peltonen, V., Tuomi, J., Klapuri, A., Fagerlund, S., Sorsa, T., & Lorho, G. (2005). Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing, 14(1), 321-329.	Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302.
Alias	instrument classification, timbre identification, instrument detection	genre recognition, music categorization, style classification
Berkaitan	5	5
Ringkasan	Instrument recognition is the task of automatically identifying which musical instruments are present in an audio recording. Formalized by Eronen et al. (2005), it addresses timbre—the tonal quality distinguishing one instrument from another. Instrument recognition is essential for music analysis, transcription, automatic indexing, and music education. It remains challenging in polyphonic contexts but has achieved good accuracy in solo and sparse accompaniment scenarios.	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.