Publication

Abstract : The fusion of music and technology has expanded rapidly. AI, machine learning, and signal processing are playing increasingly vital roles in music production, analysis, and generation. This framework introduces a comprehensive framework for automated music processing, including text-to-music generation, genre classification, and noise filtering. This model excels in complex classification tasks, efficiently identifies genre specific features, and achieves a high success rate in genre differentiation. Spectral Subtraction Noise Reduction algorithm refines the audio quality. It ensures that noise is reduced based on its unique characteristics, preserving clarity and maintaining the distinct elements that define various music styles. In conclusion, such an all-integrated system has potential for practical applications in the tasks like music indexing and retrieval or adaptive audio enhancement. Furthermore, this framework provides a basis for further improvements in intelligent music generation and classification.