International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Emotion-Based Music Therapy System |
|---|---|
| ABSTRACT | With the rapid advancement of technology, music has emerged as a powerful medium for influencing emotions and promoting mental well-being. This project presents an Emotion-Based Music Therapy System that applies artificial intelligence and machine learning techniques to detect user emotions in real time and recommend suitable music accordingly. The system analyzes facial expressions captured through a webcam using computer vision and deep learning models, particularly Convolutional Neural Networks (CNNs), to accurately classify emotional states. Based on the detected emotion, the application suggests songs intended to enhance mood, such as calming tracks for stress, uplifting melodies for sadness, and energetic music for low motivation. To strengthen personalization, users can select their preferred language, ensuring culturally relevant and emotionally appropriate recommendations. Unlike traditional music recommendation systems that depend mainly on historical listening patterns, this system dynamically adapts to the user’s current emotional condition, providing a more interactive and responsive experience. Integration with the Spotify API enables real-time access to curated playlists and tracks. Experimental observations indicate that emotion-aware music recommendations can contribute positively to mood regulation and user engagement. This project highlights the potential of AI-driven emotional intelligence in creating innovative, adaptive, and therapeutic digital music applications. |
| AUTHOR | Y. SUNAINA, Y. GAYATRI, KODAMANCHILI KUSUMA KUMARI, KAKUMANU LOKESHWAR, MUKALA KULADEEP, LOPINTI THRUSHITHA, CHIRRA NIKHIL REDDY Assistant Professor, Department of CSE (Data Science), NSRIT, Vishakhapatnam, India Student of Department of CSE (Data Science), Nadimpalli Satyanarayana Raju Institute of Technology, Visakhapatnam, Andhra Pradesh, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403054 |
| pdf/54_Emotion-Based Music Therapy System.pdf | |
| KEYWORDS | |
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