An efficient supervised framework for music mood recognition using autoencoder-based optimised support vector regression model

Abstract

Music is the art of ‘language of emotions’. Recently, music mood recognition is an emerging task. An efficient supervised framework for music mood recognition using autoencoder-based optimised support vector regression (SVR) model is developed for the music emotion recognition. Our main intention is to increase the accuracy of emotion classification of music by considering text-dependent and non-text-dependent features. For the high level feature representation, stacked autoencoder is used with two hidden layers. Modified K-Medoid-based brain storm optimisation-based support vector regression (SVR_KMBSO) model is utilised for the emotion classification. Using the K-Medoid-based brain storm algorithm, the optimal parameters of the SVR are selected. The proposed framework utilises ISMIR2012 dataset and NJU_V1 dataset for English and for Hindi; online songs are also gathered and used for the music mood recognition. All the three datasets include songs based on four emotions like happy, angry, relax and sad. The experimental results are evaluated and compared with the existing classifiers including SVR, deep belief network (DBN) and Recurrent neural network (RNN). The proposed method SVR_KMBSO achieved high accuracy using three different datasets.