  The last decade has witnessed significant advancements in deep learning-based
speech enhancement (SE). However, most existing SE research has limitations on
the coverage of SE sub-tasks, data diversity and amount, and evaluation
metrics. To fill this gap and promote research toward universal SE, we
establish a new SE challenge, named URGENT, to focus on the universality,
robustness, and generalizability of SE. We aim to extend the SE definition to
cover different sub-tasks to explore the limits of SE models, starting from
denoising, dereverberation, bandwidth extension, and declipping. A novel
framework is proposed to unify all these sub-tasks in a single model, allowing
the use of all existing SE approaches. We collected public speech and noise
data from different domains to construct diverse evaluation data. Finally, we
discuss the insights gained from our preliminary baseline experiments based on
both generative and discriminative SE methods with 12 curated metrics.
