The rapid advancement of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and generative models has created new opportunities for automating creative processes, particularly in music composition and audio production. Traditional music creation often requires musical knowledge, instrument-playing skills, professional audio-editing tools, and considerable time. These requirements can make music production challenging for beginners and individuals without formal musical training. To address these challenges, this research proposes an AI-Based Voice-to-Music Generation System that enables users to create musical compositions through natural voice commands.

The proposed system aims to integrate speech recognition, natural language processing, audio signal processing, and AI-based music generation into a unified framework. Users can provide voice commands through a microphone or upload audio recordings describing their musical preferences. The system processes the input using speech recognition techniques to convert speech into text. Natural Language Processing is then applied to identify relevant musical characteristics, including genre, mood, tempo, and instrument preferences. Based on the extracted information, an appropriate generative music model produces a musical composition corresponding to the user's instructions.

 

 

The generated audio is subsequently processed and made available for playback, saving, and downloading through an interactive user interface. The effectiveness of the proposed system will be investigated through experiments evaluating speech recognition accuracy, musical command interpretation, music generation quality, processing time, and user satisfaction.

 

Keywords— Artificial Intelligence, Voice-to-Music Generation, Natural Language Processing, Speech Recognition, Machine Learning, Deep Learning, Generative Music Models, Audio Signal Processing, Music Composition.