  In acoustic scene classification (ASC), acoustic features play a crucial role
in the extraction of scene information, which can be stored over different time
scales. Moreover, the limited size of the dataset may lead to a biased model
with a poor performance for records from unseen cities and confusing scene
classes. In order to overcome this, we propose a long-term wavelet feature that
requires a lower storage capacity and can be classified faster and more
accurately compared with classic Mel filter bank coefficients (FBank). This
feature can be extracted with predefined wavelet scales similar to the FBank.
Furthermore, a novel data augmentation scheme based on generative adversarial
neural networks with auxiliary classifiers (ACGANs) is adopted to improve the
generalization of the ASC systems. The scheme, which contains ACGANs and a
sample filter, extends the database iteratively by splitting the dataset,
training the ACGANs and subsequently filtering samples. Experiments were
conducted on datasets from the Detection and Classification of Acoustic Scenes
and Events (DCASE) challenges. The results on the DCASE19 dataset demonstrate
the improved performance of the proposed techniques compared with the classic
FBank classifier. Moreover, the proposed fusion system achieved first place in
the DCASE19 competition and surpassed the top accuracies on the DCASE17
dataset.
