  Domain mismatch is a noteworthy issue in acoustic event detection tasks, as
the target domain data is difficult to access in most real applications. In
this study, we propose a novel CNN-based discriminative training framework as a
domain compensation method to handle this issue. It uses a parallel CNN-based
discriminator to learn a pair of high-level intermediate acoustic
representations. Together with a binary discriminative loss, the discriminators
are forced to maximally exploit the discrimination of heterogeneous acoustic
information in each audio clip with target events, which results in a robust
paired representations that can well discriminate the target events and
background/domain variations separately. Moreover, to better learn the
transient characteristics of target events, a frame-wise classifier is designed
to perform the final classification. In addition, a two-stage training with the
CNN-based discriminator initialization is further proposed to enhance the
system training. All experiments are performed on the DCASE 2018 Task3
datasets. Results show that our proposal significantly outperforms the official
baseline on cross-domain conditions in AUC by relative $1.8-12.1$% without any
performance degradation on in-domain evaluation conditions.
