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Using AI Tools to Enhance Home Music Production: A Practical Guide for Independent Artists
Abstract
This chapter explores the integration of artificial intelligence tools in home music production and examines how independent artists adopt generative language models, AI-assisted mixing platforms, automated mastering systems, and text-to-music applications within decentralized studio settings. The purpose of this chapter is to analyze adoption patterns, multidimensional perceived impact, and predictors of satisfaction in AI-supported workflows. Using quantitative analysis of independent producers, the chapter evaluates how workflow efficiency, creative expansion, technical optimization, and learning acceleration shape overall satisfaction. Results indicate that perceived augmentation is the strongest predictor of positive evaluation, while production experience shows minimal influence. The findings support a Hybrid Creative Augmentation Framework and contribute to broader discussions on computational creativity, digital transformation, and the evolving role of AI as a collaborative infrastructure in contemporary music production ecosystems.
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