Linganisha mbinu

	Uchimbaji wa Melodi	Uainishaji wa Jinsia ya Muziki
Nyanja	Upataji wa Taarifa za Muziki	Upataji wa Taarifa za Muziki
Familia	Machine learning	Machine learning
Mwaka wa asili	2008	2002
Mwanzilishi	Anssi Klapuri	George Tzanetakis
Aina	Polyphonic audio analysis	Audio feature-based classification
Chanzo asilia	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.
Majina mbadala	pitch contour extraction, melodic line extraction, f0 tracking	genre recognition, music categorization, style classification
Zinazohusiana	5	5
Muhtasari	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.