Evaluating Deep Learning Models for Music Emotion Recognition

Abstract

Music listening helps people not only for entertainment, but also to reduce emotional stress in their daily lives. People nowadays tend to use online music streaming services such as Spotify, Amazon Music, Google Play Music, etc. rather than storing the songs on their devices. The songs in these streaming services are categorized into different emotional labels such as happy, sad, romantic, devotional, etc. In the music streaming applications, the songs are manually tagged with their emotional categories for music recommendation. Considering the growth of music on different social media platforms and the internet, the need for automatic tagging will increase in coming time. The work presented deals with training the deep learning model for automatic emotional tagging. It covers implementation of two different deep learning architectures for classifying the audio files using the Mel-spectrogram of music audio. The first architecture proposed is Convolutional Recurrent Model (CRNN) and the second architecture is a Parallel Convolutional Recurrent Model (Parallel CNN). Both the architectures exploit the combined features of Convolutional and Recurrent layers. This combination is used to extract features from time and frequency domains. The results with accuracies in the range of 51 to 54 % are promising for both models for a small dataset of 138 songs, considering the large datasets required for training deep learning models.