Tutorial Explores Generative Models for Music Audio Creation

This tutorial paper provides a comprehensive overview of generative models used for music audio creation, targeting readers familiar with neural network training but new to music generation. It details various methods including neural audio codecs, autoencoders, autoregressive and masked token models, diffusion models, and flow matching. The paper also covers practical aspects such as sampling acceleration, preference optimization, and connects these techniques to published systems with worked examples. AI

IMPACT Provides a foundational resource for researchers and developers entering the field of AI-driven music generation.

RANK_REASON This is a tutorial paper on generative models for music audio, submitted to arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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