Music Therapy for Transforming Human Negative Emotions: Deep Learning Approach

The paper presents the music therapy approach on controlling and transforming the human emotions. The proposed approach considers six basic emotions such as happy, sad, angry, fear, depression, and surprise along with neutral expression. Here, happy, surprise, and neutral are considered as positive emotions and sad, angry, fear, and depression are considered as negative emotions. Initially, the proposed approach used FER-(2013) dataset for training Convolution Neural Network (CNN) for facial expression recognition. Later, the proposed approach uses real-time video clips to analyze the emotions. In the first stage, the zeroth frame of the video clip is considered and preprocessed. Later the image frames are proceded for CNN for feature extraction and facial expression classification. For the negative emotions related facial expresssions, related category of music is played for certain time. It is noticed that facial deformation with respect to features such as eye-eyebrows, nose, and lips will change that exhibits emotion transformation. These changes are analyzed, and when music stops, the proposed approach extracts (n + T)th frame and proceeded for deep learning using CNN for facial expression classification. The experiments are performed and noticed that the proposed approach of music therapy has greater impact on transforming internal state of human emotions and supports in avoiding the future disasters.