  Acoustic Scene Classification (ASC) is one of the core research problems in
the field of Computational Sound Scene Analysis. In this work, we present
SubSpectralNet, a novel model which captures discriminative features by
incorporating frequency band-level differences to model soundscapes. Using
mel-spectrograms, we propose the idea of using band-wise crops of the input
time-frequency representations and train a convolutional neural network (CNN)
on the same. We also propose a modification in the training method for more
efficient learning of the CNN models. We first give a motivation for using
sub-spectrograms by giving intuitive and statistical analyses and finally we
develop a sub-spectrogram based CNN architecture for ASC. The system is
evaluated on the public ASC development dataset provided for the "Detection and
Classification of Acoustic Scenes and Events" (DCASE) 2018 Challenge. Our best
model achieves an improvement of +14% in terms of classification accuracy with
respect to the DCASE 2018 baseline system. Code and figures are available at
https://github.com/ssrp/SubSpectralNet
