APPLICATION OF DEEP LEARNING ALGORITHMS IN MUSICOLOGY: AN OVERVIEW

Abstract

Nowadays, music could be an exceptionally important and perhaps indivisible portion of people's way of life. Music involves various aspects right from generation, its genre detection to its transcription. Composing music may be an exceptionally intrusive challenge that tests the composer’s inventive capacity, whether it is a human or a computer. In spite of the fact that there have been many arguments on the matter, nearly all of the music is some regurgitation or change of a sonic idea created before. Thus, with sufficient information and the proper algorithm, deep learning ought to be able to create music that would sound human. There are numerous genres in musicology which are very different from each other, coming about in individuals having diverse inclinations in musicology. It is a critical and an important issue to classify music and to suggest individuals modern music in applications and stages. Automated music transcription is considered by many researchers to be a key empowering innovation in audio processing. Be that as it may, the execution of translation frameworks is still altogether underneath that of a human master, and correctness detailed in later a long time appears to have come to a constraint, in spite of the fact that the field is dynamic. In the paper, we try to analyse different algorithms enabling music generation, genre classification and music transcription.