  Environmental Sound Classification (ESC) is a rapidly evolving field that
recently demonstrated the advantages of application of visual domain techniques
to the audio-related tasks. Previous studies indicate that the domain-specific
modification of cross-domain approaches show a promise in pushing the whole
area of ESC forward.
  In this paper, we present a new time-frequency transformation layer that is
based on complex frequency B-spline (fbsp) wavelets. Being used with a
high-performance audio classification model, the proposed fbsp-layer provides
an accuracy improvement over the previously used Short-Time Fourier Transform
(STFT) on standard datasets. We also investigate the influence of different
pre-training strategies, including the joint use of two large-scale datasets
for weight initialization: ImageNet and AudioSet. Our proposed model
out-performs other approaches by achieving accuracies of 95.20 % on the ESC-50
and 89.14 % on the UrbanSound8K datasets.
  Additionally, we assess the increase of model robustness against additive
white Gaussian noise and reduction of an effective sample rate introduced by
the proposed layer and demonstrate that the fbsp-layer improves the model's
ability to withstand signal perturbations, in comparison to STFT-based
training. For the sake of reproducibility, our code is made available.
