Predictive Music Based on Mood

Ganesh B. Regulwar, Nikhila Kathirisetty
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Abstract

It is often difficult for a person to choose which mu- sic to listen to from a vast array of available options. Relatively, this paper focuses on building an efficient music recommendation system based on the user’s mood which determines the emotion of user using Facial Recognition technique. The model is build using the transfer learning approach for which MobileNet model and Cascade classifier are used. Analyzing the user’s face expression might help you better comprehend their current emotional or mental condition. Music and video are one area where there is a lot of potential to present clients with a variety of options depending on their interests and data. More than 60% of users anticipate that the number of songs in their music collection will grow to the point where they will be unable to find the song they need to play at some point in the future. The user would save time by not having to search for or look up tunes. The image of the user is captured using a webcam. Then, depending on the user’s mood, an appropriate song from the user’s playlist or a movie is shown.
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基于情绪的预测音乐
对于一个人来说,从大量的可选项中选择听哪首音乐往往是一件困难的事。相对而言,本文的重点是基于用户的情绪建立一个高效的音乐推荐系统,该系统利用面部识别技术确定用户的情绪。该模型采用迁移学习方法建立,其中使用了 MobileNet 模型和级联分类器。分析用户的面部表情可以帮助你更好地理解他们当前的情绪或精神状况。在音乐和视频领域,根据客户的兴趣和数据为他们提供多种选择大有可为。超过 60% 的用户预计,他们的音乐收藏中的歌曲数量会越来越多,以至于在未来的某个时间无法找到他们需要播放的歌曲。用户无需搜索或查找歌曲,从而节省了时间。用户的图像通过网络摄像头捕捉。然后,根据用户的心情,播放用户播放列表中的适当歌曲或电影。
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