  The performance of sound event detection methods can significantly degrade
when they are used in unseen conditions (e.g. recording devices, ambient
noise). Domain adaptation is a promising way to tackle this problem. In this
paper, we present VOICe, the first dataset for the development and evaluation
of domain adaptation methods for sound event detection. VOICe consists of
mixtures with three different sound events ("baby crying", "glass breaking",
and "gunshot"), which are over-imposed over three different categories of
acoustic scenes: vehicle, outdoors, and indoors. Moreover, the mixtures are
also offered without any background noise. VOICe is freely available online
(https://doi.org/10.5281/zenodo.3514950). In addition, using an
adversarial-based training method, we evaluate the performance of a domain
adaptation method on VOICe.
