A Novel Framework for Automatic Music Generation Using ‎Hybrid AI Techniques

  • Authors

    • V. Bhuvana Kumar Assistant Professor, Computer Science & Engineering, N.B.K.R Institute of Science and Technology, India
    • Dr. Narayana Rao Appini Professor & Head, AI& DS and IT, NBKR Institute of Science and Technology, India
    • N. Yedukondalu Assistant Professor, Computer Science & Engineering, N.B.K.R Institute of Science and Technology, India
    https://doi.org/10.14419/2eth4t83

    Received date: May 6, 2025

    Accepted date: May 18, 2025

    Published date: June 10, 2025

  • Music Generation Using; Hybrid Ai Technique; Musehybridnet; AI-Based Music

  • Abstract

    Automatic music generation using Artificial Intelligence (AI) has seen remarkable progress in recent years, especially due to the ‎development of deep learning techniques. These techniques have not only enabled computers to copy, but have also produced music creatively ‎that resembles human creations. This research introduces a novel and strong approach is MuseHybridNet- a hybrid model designed to carry ‎forward the boundaries of AI-generated music. ‎

    What sets is its unique integration of transformer-based architecture, emotional reference conditioning, and adaptive style transfer to music. ‎Each of these components plays an important role:‎

    • Transformer architecture, which has proved to be highly effective in natural language processing functions, is employed to model ‎long-term dependence in music, allowing the system to generate compositions that are structurally sound and sweetly consistent over time. ‎
    • Emotional reference allows conditioning models to generate music for a specific mood or emotion. Whether it is pleasure, ‎sadness, enthusiasm, or peace, Musehybridnet can adjust its output accordingly, resulting in music and human feeling as a result.‎
    • The adaptive style enables the transfer system to mix and originally move the music styles. For example, the model can produce a ‎classical piece with modern pop effects or create jazz compositions with an indication of electronic music. It gives users a powerful tool to ‎experiment with creative cross-style compositions.‎

    Musehybridnet is designed to work to represent both symbolic data (eg, MIDI files, which include notes, timing, and instruments) and raw audio ‎data, which allows it to capture the fine nuances of sound like texture and timbre. By combining these two data types, the model is better able to ‎understand and repeat the complexities of real-world music. ‎

    The proposed model shows that Musehybridnet continuously performs better than existing models in major regions, such as consistent models, ‎emotional accuracy, and stylistic diversity. The audience stated that the compositions produced by our model seem more natural, emotionally ‎attractive, and creatively rich compared to other AI systems. ‎

    In short, this research presents an important step in the field of AI-based music, offering a tool that supports human creativity and produces ‎music aligned with emotional and stylistic intentions, though full human-AI co-creation remains an area for future exploration.‎

    The synthesis of AI, music theory, and emotion modeling represents meaningful work since it both addresses existing gaps in issues of ‎musical expressiveness and style transfer while meaningfully contributing interdisciplinary value to areas of music cognition, therapeutic ‎sound design, and creative industry uses, such as film and interactive entertainment.

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  • How to Cite

    Kumar, V. B. ., Appini, D. . N. R. ., & Yedukondalu, N. . (2025). A Novel Framework for Automatic Music Generation Using ‎Hybrid AI Techniques. International Journal of Basic and Applied Sciences, 14(2), 151-162. https://doi.org/10.14419/2eth4t83