arXiv:2605.01790v2 Announce Type: replace-cross Abstract: A common design pattern in high-quality music generation is to handle structure and fidelity in different representation spaces: a generator first models high-level structure, followed by diffusion-based or neural decoding stages that reconstruct fine details. In this work, we explore an alternative view: both may be progressively modeled within a single deep acoustic-token hierarchy. To study this, we build a 64-layer residual vector quantization (RVQ) acoustic representation and propose a two-stage coarse-to-fine generation framework.
The continuous advancements in AI model architectures and computational power are enabling more sophisticated approaches to generative tasks, pushing the boundaries of what integrated models can achieve.
This research represents a significant step towards more efficient and higher-fidelity generative AI for complex data types like music, potentially leading to new applications and industries.
Current multi-stage high-quality music generation models might be superseded by single, progressively modeled deep acoustic-token hierarchies, simplifying the pipeline and improving coherence.
- · AI music generation platforms
- · Content creators
- · Acoustic representation researchers
- · Generative AI developers
- · Multi-stage generative model developers
- · Legacy music production techniques
- · Artists relying on traditional digital audio workstations for intricate sound de
The ability to generate high-fidelity music with fine details from a single model will accelerate content creation for various media.
This could lead to an explosion of AI-generated soundtracks, background music, and adaptive audio for games and interactive experiences.
The democratization of high-quality music generation might challenge traditional music industry structures and intellectual property norms.
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Read at arXiv cs.AI