Decoupled Latent Flow Matching for Few-Step Joint Vocal-Accompaniment Separation

2026-08-31

AI Summary

本文通过预训练的VAE和流匹配模型在紧凑的潜在空间中联合生成人声和伴奏，以解决长音乐信号分离成本高的问题。

Abstract

Generative modeling provides a flexible way to model mixture-conditioned source distributions, but iterative diffusion and flow matching models are costly for long music signals. This paper studies joint vocal-accompaniment separation through latent flow matching, where a pretrained variational autoencoder (VAE) maps mixtures and sources into a compact latent space and a flow matching model generates vocal and accompaniment latents jointly. The proposed framework decouples semantic separation from acoustic velocity prediction through a Separation Encoder and a Velocity Decoder. To reduce sampling cost, we further apply latent adversarial post-training inspired by Flow2GAN for few-step generation. Experiments show that latent adversarial refinement can improve perceptual and separation metrics under a reduced sampling budget.

Problem

• latent flow matching
• joint vocal-accompaniment separation
• iterative diffusion
• sampling cost

Innovation

• latent flow matching
• decoupled semantic separation
• velocity prediction
• latent adversarial post-training
• few-step generation