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arXiv · 2410.23005

Improving Musical Accompaniment Co-creation via Diffusion Transformers

Javier Nistal·Marco Pasini·Stefan Lattner
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Abstract

Building upon Diff-A-Riff, a latent diffusion model for musical instrument accompaniment generation, we present a series of improvements targeting quality, diversity, inference speed, and text-driven control. First, we upgrade the underlying autoencoder to a stereo-capable model with superior fidelity and replace the latent U-Net with a Diffusion Transformer. Additionally, we refine text prompting by training a cross-modality predictive network to translate text-derived CLAP embeddings to audio-derived CLAP embeddings. Finally, we improve inference speed by training the latent model using a consistency framework, achieving competitive quality with fewer denoising steps. Our model is evaluated against the original Diff-A-Riff variant using objective metrics in ablation experiments, demonstrating promising advancements in all targeted areas. Sound examples are available at: https://sonycslparis.github.io/improved_dar/.

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BibTeXRIS

Javier Nistal, Marco Pasini, Stefan Lattner. 2024-10-30. Improving Musical Accompaniment Co-creation via Diffusion Transformers. https://arxiv.org/abs/2410.23005

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