Text-to-Music Generation Using AI: Theoretical Foundations and Practical Applications

Abstract

A text-to-music generator is an artificial intelligence system that composes songs from user-provided text prompts by leveraging large datasets for training. This research explores the theoretical foundations linking language and music through semantic, emotional, and structural analysis, and demonstrates practical integration of AI music generation into software via APIs. To illustrate, simulated Python code examples are provided using a fictional Suno AI API, alongside references to platforms such as Boomy, AIVA, Amper Music, and OpenAI Jukebox. These integrations highlight how developers and businesses can embed automated music creation into applications for education, entertainment, therapy, and digital media, thereby advancing interdisciplinary research and software innovation.