  Sound Event Detection (SED) aims to predict the temporal boundaries of all
the events of interest and their class labels, given an unconstrained audio
sample. Taking either the splitand-classify (i.e., frame-level) strategy or the
more principled event-level modeling approach, all existing methods consider
the SED problem from the discriminative learning perspective. In this work, we
reformulate the SED problem by taking a generative learning perspective.
Specifically, we aim to generate sound temporal boundaries from noisy proposals
in a denoising diffusion process, conditioned on a target audio sample. During
training, our model learns to reverse the noising process by converting noisy
latent queries to the groundtruth versions in the elegant Transformer decoder
framework. Doing so enables the model generate accurate event boundaries from
even noisy queries during inference. Extensive experiments on the Urban-SED and
EPIC-Sounds datasets demonstrate that our model significantly outperforms
existing alternatives, with 40+% faster convergence in training.
