Real Time Emotion Based Music Player Using CNN Architectures

Abstract

Emotion detection is the process of detecting a human being's emotions based on various facial cues and visual information. This field has gained much traction since the popularity of deep learning. Emotion detection has also given rise to many applications that had not been thought of before. One of the areas that are heavily associated with emotions is music. Music can invoke particular emotions of the listener, and a person feeling a certain emotion would look for a similar song. We use our emotion detection model to associate these emotions with a music player that plays music that accompanies user experiences. The model we designed includes two convolutional neural networks (CNN) models: a five-layer model and a global average pooling (GAP) model. We combined these CNN models with transfer-learning models. For our transfer-learning models, we used three pre-trained models: ResNet50; SeNet50; VGG16. Our results are comparable with the state-of-the-art models; however, our models are more efficient in performance. © 2021 IEEE.