Google DeepMind's SURF Separates Audio Without Ever Hearing Clean Sounds

Sound separation has always had a dirty secret: the best models need clean, isolated audio tracks to train on. If you want a model to separate speech from background noise, you typically need thousands of hours of pristine, individually-recorded voices. That data is expensive to collect, and models trained on it often break when they hit real-world audio that doesn't match their training set. Google DeepMind's new SURF (Separation via Unsupervised Remixing Flow) paper, presented at ICML 2026 in Seoul, attacks this problem head-on: it trains entirely on raw, mixed audio with no clean sources required.

The core problem SURF solves

Single-channel source separation is genuinely hard. Given a single audio recording of two people talking over music, you want to recover each individual stream. In supervised settings where vast amounts of clean source data are available, this problem has been addressed successfully by generative diffusion and flow-based prior models. However, access to such clean source samples is often limited, and even when available, supervised models are vulnerable to domain shifts.

This matters enormously in practice. Reliance on synthetic training data is problematic because good performance depends upon the degree of match between the training data and real-world audio, especially in terms of the acoustic conditions and distribution of sources. The acoustic properties can be challenging to accurately simulate, and the distribution of sound types may be hard to replicate. In domains like bio-acoustics, hyperspectral imaging, or gravitational wave detection, obtaining clean in-domain source data is practically impossible.

How SURF actually works

Flow matching is a generative modeling technique that learns to transform one probability distribution into another by training a neural network to predict a smooth "velocity field" , essentially, a direction of travel through the space of possible signals. SURF applies this idea to source separation, but with a twist: it never sees clean sources during training.