Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (2): 115-121.doi: 10.23940/ijpe.23.02.p4.115121

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SongRec: A Facial Expression Recognition System for Song Recommendation using CNN

Shalaka Prasad Deore*   

  1. Department of Computer Engineering, M.E.S. College of Engineering, Pune, S.P. Pune University, 411007, India
  • Contact: * E-mail address:

Abstract: The music has special connection with emotion of the person. One's mood can be improved by it in a special way. The classification of the emotion of music is a difficult research area because human perception is subjective. The emotional response of the user is closely related to the music recommendation system because most music is selected based on the listener's mood. Many studies have been conducted to determine how to identify emotions using various methods. These techniques have been useful in evoking the subject's feeling using a variety of devices and other hardware that can be quite expensive and inaccurate. On the other hand, observing the person’s facial expression can be quite helpful in accurately identifying their mood or feeling. Hence the main goal of the proposed system is to identify an individual's facial emotions effectively in order to make appropriate music recommendations. The proposed system makes use of Convolutional Neural Networks (CNN) to train facial dataset to recognize various emotional reactions. This trained model is used to detect mod of the person based on facial expressions and recommend song related to that emotion. The proposed system is also optimizing the results using fuzzy classification. The results demonstrate the effectiveness of the proposed methodology.

Key words: Music, Emotion, Song Recommendation, Convolution Neural Network (CNN), Mood Identification, Fuzzy classification