  In recent years, neural network approaches have shown superior performance to
conventional hand-made features in numerous application areas. In particular,
convolutional neural networks (ConvNets) exploit spatially local correlations
across input data to improve the performance of audio processing tasks, such as
speech recognition, musical chord recognition, and onset detection. Here we
apply ConvNet to acoustic scene classification, and show that the error rate
can be further decreased by using delta features in the frequency domain. We
propose a multiple-width frequency-delta (MWFD) data augmentation method that
uses static mel-spectrogram and frequency-delta features as individual input
examples. In addition, we describe a ConvNet output aggregation method designed
for MWFD augmentation, folded mean aggregation, which combines output
probabilities of static and MWFD features from the same analysis window using
multiplication first, rather than taking an average of all output
probabilities. We describe calculation results using the DCASE 2016 challenge
dataset, which shows that ConvNet outperforms both of the baseline system with
hand-crafted features and a deep neural network approach by around 7%. The
performance was further improved (by 5.7%) using the MWFD augmentation together
with folded mean aggregation. The system exhibited a classification accuracy of
0.831 when classifying 15 acoustic scenes.
