A Survey on Autonomous Techniques for Music Classification based on Human Emotions Recognition

Abstract

Music is one of the finest element to trigger emotions in human beings. Each and every human being feels the music and emotions are automatically provoked by listening music. Music is considered as strong stress reliever. With the increase in size of music dataset available online and advancement of automation technologies the emotions from the music are to be recognized automatically so that the online database of music can be organized and browsed in an efficient manner. Automation of music emotion classification (MEC) helps the people to listen the music of their interest without wasting time on surfing the internet. It helps the psychologists in treatment process of patients. It also helps the musicians and artists to work on specific type of music and to classify them. This paper aims to provide the overview and survey related to autonomous technique for music classification (ATMC). In this article, the basic steps such as database collection, preprocessing, database analysis, feature extraction, classification and evaluation parameters involved in ATMC are explained and comprehensive review related to the basic steps is summarized. Research issues and solutions related to ATMC along with future scope are also discussed in this article.