A Novel Framework for Automatic Music Generation Using ‎Hybrid AI Techniques - Kumar, V. Bhuvana; Appini, Dr. Narayana Rao; Yedukondalu, N. (International Journal of Basic and Applied Sciences, 2025)
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.