We propose a new task, audio diarization. The motivation is that there are applications, such as audio monitoring in an unknown environment, where initially the sound event classes to be recognized are unknown. For such a scenario, we propose to first localize in time relevant sound events and to classify them, e.g., by comparing with known event classes, in a second step. This contribution is dedicated to the first step, which we call audio diarization, as it is reminiscent of the speaker diarization stage that precedes and simplifies the second stage, speech recognition, in multi-talker conversational speech processing. In this contribution, we define audio diarization as detecting onsets and offsets of sound events with overlap for an open set of classes and without user prompts. We show how a speaker diarization system can be employed for audio diarization and propose an evaluation setup. Compared to a closed set Sound Event Detection system, the proposed system achieves similar performance with the additional ability to detect novel sounds.