  Recognizing acoustic events is an intricate problem for a machine and an
emerging field of research. Deep neural networks achieve convincing results and
are currently the state-of-the-art approach for many tasks. One advantage is
their implicit feature learning, opposite to an explicit feature extraction of
the input signal. In this work, we analyzed whether more discriminative
features can be learned from either the time-domain or the frequency-domain
representation of the audio signal. For this purpose, we trained multiple deep
networks with different architectures on the Freiburg-106 and ESC-10 datasets.
Our results show that feature learning from the frequency domain is superior to
the time domain. Moreover, additionally using convolution and pooling layers,
to explore local structures of the audio signal, significantly improves the
recognition performance and achieves state-of-the-art results.
