  Recent acoustic event classification research has focused on training
suitable filters to represent acoustic events. However, due to limited
availability of target event databases and linearity of conventional filters,
there is still room for improving performance. By exploiting the non-linear
modeling of deep neural networks (DNNs) and their ability to learn beyond
pre-trained environments, this letter proposes a DNN-based feature extraction
scheme for the classification of acoustic events. The effectiveness and
robustness to noise of the proposed method are demonstrated using a database of
indoor surveillance environments.
