Generative Sound Masking

A common way to reduce the salience of disruptive background sounds is to perform sound masking: using a pleasant sound, like pink noise, to conceal the undesirable sounds. We propose a method that learns to mask future background sounds. First, we apply a novel diffusion guidance method to obtain sounds that successfully mask a given background sound, which we use as synthetic training data. Second, we use this data to train a causal diffusion model to generate mask sounds that successfully conceal future background noises. Our online model reduces background-identification accuracy in an audio-language model more than background-independent masking baselines. Additional automated metrics assess the natural-sound content and distributional similarity of the resulting mixtures.