  Environmental Sound Classification (ESC) is an active research area in the
audio domain and has seen a lot of progress in the past years. However, many of
the existing approaches achieve high accuracy by relying on domain-specific
features and architectures, making it harder to benefit from advances in other
fields (e.g., the image domain). Additionally, some of the past successes have
been attributed to a discrepancy of how results are evaluated (i.e., on
unofficial splits of the UrbanSound8K (US8K) dataset), distorting the overall
progression of the field.
  The contribution of this paper is twofold. First, we present a model that is
inherently compatible with mono and stereo sound inputs. Our model is based on
simple log-power Short-Time Fourier Transform (STFT) spectrograms and combines
them with several well-known approaches from the image domain (i.e., ResNet,
Siamese-like networks and attention). We investigate the influence of
cross-domain pre-training, architectural changes, and evaluate our model on
standard datasets. We find that our model out-performs all previously known
approaches in a fair comparison by achieving accuracies of 97.0 % (ESC-10),
91.5 % (ESC-50) and 84.2 % / 85.4 % (US8K mono / stereo).
  Second, we provide a comprehensive overview of the actual state of the field,
by differentiating several previously reported results on the US8K dataset
between official or unofficial splits. For better reproducibility, our code
(including any re-implementations) is made available.
