  Recently, convolutional neural networks (CNN) have achieved the
state-of-the-art performance in acoustic scene classification (ASC) task. The
audio data is often transformed into two-dimensional spectrogram
representations, which are then fed to the neural networks. In this paper, we
study the problem of efficiently taking advantage of different spectrogram
representations through discriminative processing strategies. There are two
main contributions. The first contribution is exploring the impact of the
combination of multiple spectrogram representations at different stages, which
provides a meaningful reference for the effective spectrogram fusion. The
second contribution is that the processing strategies in multiple frequency
bands and multiple temporal frames are proposed to make fully use of a single
spectrogram representation. The proposed spectrogram processing strategies can
be easily transferred to any network structures. The experiments are carried
out on the DCASE 2020 Task1 datasets, and the results show that our method
could achieve the accuracy of 81.8% (official baseline: 54.1%) and 92.1%
(official baseline: 87.3%) on the officially provided fold 1 evaluation dataset
of Task1A and Task1B, respectively.
