Multi-label classifier for emotion recognition from music

Abstract

Music is one of the important medium to express the emotions such as anger, happy, sad, amazed, quiet etc. In this paper, we consider the task of emotion recognition from music as a multi-label classification task because a piece of music may have more than one emotion at the same time. This research work proposes the Binary Relevance (BR) based Least Squares Twin Support Vector Machine (LSTSVM) multi-label classifier for emotion recognition from music. The performance of the proposed classifier is compared with the eight existing multi-label learning methods using fourteen evaluation measures in order to evaluate it from different point of views. The experimental result suggests that the proposed multi-label classifier based emotion recognition system is more efficient and gives satisfactory outcomes over the other existing multi-label classification approaches.