  In this paper we propose a novel environmental sound classification approach
incorporating unsupervised feature learning from codebook via spherical
$K$-Means++ algorithm and a new architecture for high-level data augmentation.
The audio signal is transformed into a 2D representation using a discrete
wavelet transform (DWT). The DWT spectrograms are then augmented by a novel
architecture for cycle-consistent generative adversarial network. This
high-level augmentation bootstraps generated spectrograms in both intra and
inter class manners by translating structural features from sample to sample. A
codebook is built by coding the DWT spectrograms with the speeded-up robust
feature detector (SURF) and the K-Means++ algorithm. The Random Forest is our
final learning algorithm which learns the environmental sound classification
task from the clustered codewords in the codebook. Experimental results in four
benchmarking environmental sound datasets (ESC-10, ESC-50, UrbanSound8k, and
DCASE-2017) have shown that the proposed classification approach outperforms
the state-of-the-art classifiers in the scope, including advanced and dense
convolutional neural networks such as AlexNet and GoogLeNet, improving the
classification rate between 3.51% and 14.34%, depending on the dataset.
