  In this paper, we propose a model for the Environment Sound Classification
Task (ESC) that consists of multiple feature channels given as input to a Deep
Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the
paper lies in using multiple feature channels consisting of Mel-Frequency
Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC),
the Constant Q-transform (CQT) and Chromagram. Such multiple features have
never been used before for signal or audio processing. And, we employ a deeper
CNN (DCNN) compared to previous models, consisting of spatially separable
convolutions working on time and feature domain separately. Alongside, we use
attention modules that perform channel and spatial attention together. We use
some data augmentation techniques to further boost performance. Our model is
able to achieve state-of-the-art performance on all three benchmark environment
sound classification datasets, i.e. the UrbanSound8K (97.52%), ESC-10 (95.75%)
and ESC-50 (88.50%). To the best of our knowledge, this is the first time that
a single environment sound classification model is able to achieve
state-of-the-art results on all three datasets. For ESC-10 and ESC-50 datasets,
the accuracy achieved by the proposed model is beyond human accuracy of 95.7%
and 81.3% respectively.
