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SAMUeL: Efficient Vocal-Conditioned Music Generation via Soft Alignment Attention and Latent Diffusion

Hei Shing Cheung, Boya Zhang, Jonathan H. Chan

TL;DR

SAMUeL introduces a vocal-conditioned music generation framework built on latent diffusion with a novel soft alignment attention that balances local and global temporal dependencies. Operating in a compressed latent space derived from a pre-trained VAE, the model achieves extreme efficiency with about 15M parameters and 800-timestep diffusion, enabling real-time performance on consumer hardware. It directly consumes vocal melodies to produce instrument accompaniments, achieving competitive production quality and content unity while outperforming OpenAI Jukebox in key perceptual metrics despite significantly smaller size. The work demonstrates a practical path toward accessible, interactive AI-assisted music creation, with potential extensions in longer contexts, richer timbre, and multi-modal conditioning.

Abstract

We present a lightweight latent diffusion model for vocal-conditioned musical accompaniment generation that addresses critical limitations in existing music AI systems. Our approach introduces a novel soft alignment attention mechanism that adaptively combines local and global temporal dependencies based on diffusion timesteps, enabling efficient capture of multi-scale musical structure. Operating in the compressed latent space of a pre-trained variational autoencoder, the model achieves a 220 times parameter reduction compared to state-of-the-art systems while delivering 52 times faster inference. Experimental evaluation demonstrates competitive performance with only 15M parameters, outperforming OpenAI Jukebox in production quality and content unity while maintaining reasonable musical coherence. The ultra-lightweight architecture enables real-time deployment on consumer hardware, making AI-assisted music creation accessible for interactive applications and resource-constrained environments.

SAMUeL: Efficient Vocal-Conditioned Music Generation via Soft Alignment Attention and Latent Diffusion

TL;DR

SAMUeL introduces a vocal-conditioned music generation framework built on latent diffusion with a novel soft alignment attention that balances local and global temporal dependencies. Operating in a compressed latent space derived from a pre-trained VAE, the model achieves extreme efficiency with about 15M parameters and 800-timestep diffusion, enabling real-time performance on consumer hardware. It directly consumes vocal melodies to produce instrument accompaniments, achieving competitive production quality and content unity while outperforming OpenAI Jukebox in key perceptual metrics despite significantly smaller size. The work demonstrates a practical path toward accessible, interactive AI-assisted music creation, with potential extensions in longer contexts, richer timbre, and multi-modal conditioning.

Abstract

We present a lightweight latent diffusion model for vocal-conditioned musical accompaniment generation that addresses critical limitations in existing music AI systems. Our approach introduces a novel soft alignment attention mechanism that adaptively combines local and global temporal dependencies based on diffusion timesteps, enabling efficient capture of multi-scale musical structure. Operating in the compressed latent space of a pre-trained variational autoencoder, the model achieves a 220 times parameter reduction compared to state-of-the-art systems while delivering 52 times faster inference. Experimental evaluation demonstrates competitive performance with only 15M parameters, outperforming OpenAI Jukebox in production quality and content unity while maintaining reasonable musical coherence. The ultra-lightweight architecture enables real-time deployment on consumer hardware, making AI-assisted music creation accessible for interactive applications and resource-constrained environments.

Paper Structure

This paper contains 21 sections, 7 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Diffusion Models for Audio Generation
  4. Conditional Audio Generation and Attention Mechanisms
  5. Methodology
  6. Problem Formulation
  7. Latent Space Encoding
  8. Data Collection
  9. Model Architecture
  10. U-Net Design
  11. Soft Alignment Attention Mechanism
  12. Architecture Specifications and Training Objective
  13. Training and Implementation
  14. Experimental Results
  15. Evaluation Methodology and Quantitative Results
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Detailed soft alignment attention mechanism computing both local and global attention patterns. The dual-scale approach captures fine-grained temporal dependencies within local windows while maintaining global musical coherence through full-sequence attention, critical for maintaining musical structure and quality. Note that RoPE is not applied to for local attention, learning about the general local acoustic relationships.
  • Figure 2: Complete model architecture showing U-Net backbone with multi-scale attention mechanisms. Vocal conditioning integrates through cross-attention at multiple stages, while FiLM layers provide fine-grained temporal control.
  • Figure 3: Standard deviation of sample prediction $v_\theta$ across diffusion timesteps. High variance during early timesteps reflects global structure learning, while convergence during later timesteps indicates fine-detail refinement.