AI-Based Music Therapy Recommendation System

This paper is focused on a music recommendation system that utilizes intelligence. The system suggests music based on people's emotional state through analysis. The FER2013 dataset is utilized to determine people's emotions through expressions. Our experiments demonstrate that the system is more proficient at analyzing people's emotional state by incorporating information into its data analysis. The system was accurate at 38.127 percent initially... It was 63.527 percent accurate after including more data. This is an improvement. It had the highest accuracy level of 63.110 percent after only 393 attempts. After 100 tries, the score was still pretty good, with only 62.051 percent remaining. People can be suggested music by emotion recognition, as demonstrated by the results. The system's functionality must be tested to ensure its reliability. To gauge people's opinions on the music suggested by our system, we must question their judgment. We will improve the system in due time by implementing diverse emotional recognition models and utilizing more real-world data. The system will be tailored to suit the needs of every user. Through the use of emotion recognition and music preference data, the music recommendation system can suggest songs to its users.

[1]    Devarajan et al., “Facial Emotion-Based Music Recommendation,” as cited in the supplied literature. [2]           Tran et al., “Emotion-Aware Music Recommendation,” as cited in the supplied literature. [3]    Kumar et al., “Facial Emotion Recognition and Music Recommendation Using CNN,” as cited in the supplied literature. [4]     Aswal et al., “Facial-Expression-Based Musical Therapy System,” as cited in the supplied literature. [5]   Radha et al., “Facial Emotion Recognition Using MobileNet,” as cited in the supplied literature. [6]     Devi et al., “Xpressify: CNN-Based Emotion Recognition and Music Recommendation,” as cited in the supplied literature. [7]     Bakariya et al., “CNN-Based Facial Emotion Recognition and Music Recommendation,” as cited in the supplied literature. [8]   Kurniawan et al., “Facial-Expression-Based Music Recommendation Using Deep Learning,” as cited in the supplied literature. [9]  Adru and Johnson, “Facial Expression-Based Music Recommendation Using Transfer Learning and LightGBM,” as cited in the supplied literature. [10]    Wankhade et al., “Deep Learning-Based Music Recommendation Using Emotion and User Preferences,” as cited in the supplied literature.