Speech Emotion Based Music Recommendation

Abstract

Music has always been a very popular form of entertainment and many pioneers in the tech industry have quickly realised this. With the growth of music streaming platforms, users have a multitude of options to choose from. This calls for a system that makes music organization and search management easier. This work uses Siamese network, a meta learning model, to recognize emotions in speech, based on which music recommendations are made. We have used RAVDESS dataset for emotion recognition and music mood classification dataset. The user emotions are mapped to appropriate moods of songs and used for recommendations.