A Music Recommendation System based on Emotion, Age, and Ethnicity is developed in this study, using FER-2013 and ``Age, Gender, and Ethnicity (Face Data) CSV'' datasets. The CNN architecture, which is extensively used for this kind of purpose has been applied to the training of the models. After adding several appropriate layers to the training end of the project, in total, 3 separate models are trained in the Deep Learning side of the project: Emotion, Ethnicity, and Age. After the training step of these models, they are used as classifiers on the web application side. The snapshot of the user taken through the interface is sent to the models to predict their mood, age, and ethnic origin. According to these classifiers, various kinds of playlists pulled from Spotify API are proposed to the user in order to establish a functional and user-friendly atmosphere for the music selection. Afterward, the user can choose the playlist they want and listen to it by following the given link.
Exploring the correlation between the type of music and the emotions evoked: A study using subjective questionnaires and EEG
Audio Acoustics Lab - Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology (GUT), Poland（电子电信与信息学院，格但斯克技术大学（GUT））；School of Information Technology, Halmstad University, Sweden（信息科技学院，哈马碧大学）
Doctoral Program in Electrical and Computer Engineering（电子与计算机工程博士项目）；Faculty of Engineering, University of Porto（葡萄牙波尔图大学工程学院）；Faculty of Science and Technology, University of Coimbra（科英布拉大学科学与技术学院）；Frost School of Music, University of Miami（迈阿密大学弗罗斯特音乐学院）；Faculty of Science and Technology, NOVA University Lisbon（里斯本NOVA大学科学与技术学院）；ISEP, Polytechnic of Porto（波尔图理工学院ISEP）
