Using Crowd Sourced Data for Music Mood Classification

Music has been part of human lives since ancient times. We have hundreds of millions of songs representing different cultures, mood and genres. These songs are readily accessible using Internet and streaming services. However, the discovery of the right music piece to listen is hard and an automated assistance to find the right song among the millions is always desired. There have been several attempts to classify music on the basis of their genres but their efforts have not been much fruitful because of lack of good and large datasets. Moreover, identifying the set of features to represent the music in a summarized way is also a challenging task. In this work, we present an automated music mood classification approach that uses crowd-sourced platforms to label the songs. It eliminates the subjectivity of one’s perception of mood on a song. We have confined our work to two classes of mood: happy and sad. The proposed approach is tested with three machine learning models: artificial neural networks (ANN), Decision Tree (DT) and Support Vector Machine (SVM). The experimental results show that ANN performs better than DT and SVM.