论文标题
音乐推荐系统基于情感，年龄和种族
Music Recommendation System based on Emotion, Age and Ethnicity
论文作者
论文摘要
在本研究中，使用FER-2013和``年龄，性别和种族（面部数据）CSV''开发了基于情感，年龄和种族的音乐推荐系统。 CNN体系结构已广泛用于此类目的，已应用于模型的训练。在项目的培训端添加了几层适当的层后，总共3种单独的模型在项目的深度学习方面进行了培训：情感，种族和年龄。在这些模型的培训步骤之后，它们被用作Web应用程序端的分类器。通过界面吸收的用户的快照将发送到模型，以预测其心情，年龄和种族。根据这些分类器，向用户提出了从Spotify API中提取的各种播放列表，以建立功能性和用户友好的氛围来进行音乐选择。之后，用户可以选择他们想要的播放列表，并通过遵循给定的链接来收听。
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.
