Comparative Study of Musical Performance by Machine Learning

Abstract

This paper deals with the very special domains from computer science viz. Machine learning, g enetic algorithms, rule based systems, music and various intelligent systems . Most of the musicians use m achine l earning approach to improve accuracy of the musical note . Intelligent systems use databases to store monophonic audio recordings performed by the musician of jazz standards. Howeve r, these approach use to obtain a model which explai n and generate performances of expressive music. Rule based approach gives note level information containing time, dynamics and melody alteration . In this paper, we investigate how all these machine learning techniques work . We also compare their featu res and performance with evolutionary approach which will help user to get Rule based incremental model . Finally, output will be in a summarized format which gives reference solution. Comparative analysis shows that methods used by Incremental Rule b ased Appro ach provide full functionality and effectiveness as compared with previous machine learning techniques .