Emotionrhythms: A Real-Time Music Player Based on Emotion Recognition

Emotionrhythms: A Real-Time Music Player Based on Emotion Recognition

Yashashri Kamalakant Gosavi

 PG Student, Dept. of Information Technology, Patkar-Varde College,

Mumbai, Maharashtra, India

Abstract - EmotionRhythms is a groundbreaking music player designed to automatically curate and play songs that align with the user's current emotional state. In today's fast-paced world, music holds a significant place in people's lives, often serving as a reflection of their emotions. However, traditional music players require users to manually sift through extensive playlists to find songs that match their mood, which can be time-consuming and frustrating. Recognizing that individuals frequently associate music with their emotional experiences, a system that intelligently organizes and recommends songs based on the user's emotional state would offer immense value. This innovative solution allows users to effortlessly enjoy music that resonates with their feelings, enhancing their listening experience. The proposed system focuses on four primary emotions—Happy, Sad, Angry, Fear, Disgust, Surprise and Neutral—which are detected using Convolutional Neural Networks (CNN), a machine learning technique specialized in facial expression analysis. Once the user's emotion is identified, the system seamlessly plays recommended songs from predefined directories, ensuring a personalized and emotionally attuned music experience.

Keywords— Convolutional Neural Networks (CNN), Emotion Recognition, Facial Expression Analysis, Music Recommendation System, Real-Time Emotion Detection, OpenCV