Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 9 No 4 (2025): Volume 9, Issue 4, April 2025 | Pages: 140-146
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 23-04-2025
BeatLens is a song recommendation engine based on AI that is created to boost social media storytelling through the automation of music selection for Instagram stories. It solves the typical problem of taking too much time to select songs by using uploaded images via sophisticated computer vision models such as YOLO (for object detection) and CLIP (for scene classification) to decipher visual context. The system then uses Large Language Models (LLMs) like LLaMA 3, LLaVA, and Mistral to suggest songs based on the mood, theme, and setting of the image. For maximum accessibility, BeatLens is available in 14 languages, namely English, Marathi, Hindi, Spanish, Punjabi, Bhojpuri, Korean, German, Portuguese, Japanese, Tamil, Telugu, Kannada, and Malayalam. This multilingual functionality, paired with its AI-powered analysis, turns song choosing into an intuitive, streamlined process—improving user experience and minimizing decision fatigue.
AI-powered Recommendation, Image Analysis, Object Detection, Scene Recognition, LLM, User Experience, YOLO
Aditya Arolkar, Dhaval Smart, Gaurav Waghmare, Pratham Atale, & Prof. Sonali Despande. (2025). BeatLens: A Context-Aware Vision-to-Music Framework for Image-Based Song Recommendations. International Research Journal of Innovations in Engineering and Technology - IRJIET, 9(4), 140-146. Article DOI https://doi.org/10.47001/IRJIET/2025.904021
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