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@article{173217,
        author = {Anshu  Sakhare and Lekha Pulavarthy and Riya kamble and Shruti Parade and Prof. Amit Narote},
        title = {AI-DRIVEN DANCE GENERATION AND MUSIC COMPOSITION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2813-2816},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173217},
        abstract = {The field of AI-generated music and dance is experiencing rapid growth, employing deep learning and generative models to produce synchronized sound and movement. This research investigates recent innovations in AI-based music continuation and choreography creation, with an emphasis on sequence modelling and motion prediction methods. We investigate cutting-edge approaches for extending musical pieces using LSTM-based models and creating dance movements in response to input music through machine learning algorithms. Primary areas of investigation include data pre-processing techniques, music feature extraction, and motion synchronization strategies. A comparative evaluation of neural networks for music and dance generation assesses model efficiency, synchronization precision, and artistic consistency. Additionally, we examine multimodal fusion techniques that improve the integration of auditory and visual signals, ensuring smooth and natural dance choreography. This study aims to produce dynamic dance movements from given music samples and create extended compositions from brief musical segments, utilizing deep learning techniques in sequence modelling.},
        keywords = {AI-generated choreography, Music composition, Deep learning, AIST++, Long Short-Term Memory (LSTM), Virtual Reality (VR), Motion Capture.},
        month = {February},
        }