  Due to the variability in characteristics of audio scenes, some scenes can
naturally be recognized earlier than others. In this work, rather than using
equal-length snippets for all scene categories, as is common in the literature,
we study to which temporal extent an audio scene can be reliably recognized
given state-of-the-art models. Moreover, as model fusion with deep network
ensemble is prevalent in audio scene classification, we further study whether,
and if so, when model fusion is necessary for this task. To achieve these
goals, we employ two single-network systems relying on a convolutional neural
network and a recurrent neural network for classification as well as early
fusion and late fusion of these networks. Experimental results on the
LITIS-Rouen dataset show that some scenes can be reliably recognized with a few
seconds while other scenes require significantly longer durations. In addition,
model fusion is shown to be the most beneficial when the signal length is
short.
