Abstract: The development of a Music Recommendation System involved the utilization of the FER-2013 and Age, Gender (Facial Data) datasets. The system utilizes the CNN architecture, commonly employed for such purposes, to train three separate models: Emotion, Gender, and Age. To enhance the models' performance, additional layers are incorporated into the training phase. These models are subsequently employed as classifiers. To predict the user's mood, age, and gender, a snapshot of the user captured through the camera is forwarded to the trained models. Based on the outcomes of these classifiers, various playlists sourced from a database are suggested to the user. The goal is to create a functional and user-friendly environment for music selection. Once the playlists are proposed, the user can select their desired playlist and begin listening to the recommended music.
Keywords: Deep Learning, CNN, Emotion, Age, Gender, Music Recommendation System.
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DOI:
10.17148/IJARCCE.2023.124144
[1] Mr Chakrapani D S, Sidrath Iram,Suchitra R Bhat Agni, Supritha L, Leelavathi S, "MUSIC RECOMMENDATION BASED ON FACIAL EMOTION RECOGNITION," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2023.124144
