Context-Aware Music Embedding in Silent Videos Leveraging Transformer Architectures: A Review

Authors- Research Scholar Badhe Om Ghanshyambhai

Abstract-This paper gives an excellent assessment of context-conscious track embedding in silent films using Transformer architectures. The study addresses the critical undertaking of dynamically integrating suit- able musical accompaniment on video content by way of using advanced deep studying techniques. We discover the evolution from traditional strategies the use of RNNs and CNNs to fashionable Trans-former-primarily based solutions, focusing on actual-time processing and emotional coherence. The studies examine diverse methodologies, together with the Vision Transformer algorithm for video analysis and context know-how, in conjunction with sophisticated tune era strategies. Our proposed frame- work consists of a three-phase approach: video evaluation, song technology, and integration, with unique emphasis on keeping temporal align- ment and emotional consistency. The assessment framework encompasses a couple of parameters, consisting of Detection Ac-curacy Rate , Emotional Coherence Score , and Synchronization Accuracy , providing a sturdy evaluation technique. The evaluation additionally identifies modern-day boundaries in existing systems and proposes future guidelines for studies, which includes multi-modal enhancement and personalization features. This work contributes to the growing subject of AI-driven multimedia processing with the aid of imparting an established technique to context-conscious music embedding, capacity reaping re- wards each instructional researchers and industry practitioners in developing more state-of-the-art audio-visual content generation systems.

DOI: /10.61463/ijset.vol.13.issue1.164