Music Analysis Using Machine Learning

Music Analysis Using Machine Learning

Prof.S.V.Phulari#1, Rushikesh Borkar#2,  Rihanraza Inamdar#3, Shirish Khengat#4, Gaurang Karande#5,

#Computer Engineering Department, Savitribai Phule University
Manjari (BK), Pune - 412307(Maharashtra) India

Abstract— Interaction with computers in musical performances is very much limited by a lack of music understanding by computers. If computers do not understand musical structures such as rhythmic units, chords, keys, and phrases, then interaction with computers will necessarily be difficult and cumbersome. Research into Music Understanding by computer aims to raise the level of human computer interaction in musical tasks including live music performance.

Automatic music analysis is the automated extraction of relevant perceptual information (notes, instruments, etc.) from music files (like mp3s). First attempted in the 1970s at Stanford University [Moorer], it remains an unsolved problem. The problem is highly multifaceted and interdisciplinary, requiring the extraction of musical notes, instruments, percussion, emotion, etc., and drawing from fields as varied as computer science, mathematics, biology, physics, psychology, and electrical engineering. The problem's difficulty lies in a necessity to reverse-engineer the human brain.

Keywords— Melody Analysis, Chord Analysis, Structure Analysis, Machine Learning, Audio Systems.