  Data augmentation is a valuable tool for the design of deep learning systems
to overcome data limitations and stabilize the training process. Especially in
the medical domain, where the collection of large-scale data sets is
challenging and expensive due to limited access to patient data, relevant
environments, as well as strict regulations, community-curated large-scale
public datasets, pretrained models, and advanced data augmentation methods are
the main factors for developing reliable systems to improve patient care.
However, for the development of medical acoustic sensing systems, an emerging
field of research, the community lacks large-scale publicly available data sets
and pretrained models. To address the problem of limited data, we propose a
conditional generative adversarial neural network-based augmentation method
which is able to synthesize mel spectrograms from a learned data distribution
of a source data set. In contrast to previously proposed fully convolutional
models, the proposed model implements residual Squeeze and Excitation modules
in the generator architecture. We show that our method outperforms all
classical audio augmentation techniques and previously published generative
methods in terms of generated sample quality and a performance improvement of
2.84% of Macro F1-Score for a classifier trained on the augmented data set, an
enhancement of $1.14\%$ in relation to previous work. By analyzing the
correlation of intermediate feature spaces, we show that the residual Squeeze
and Excitation modules help the model to reduce redundancy in the latent
features. Therefore, the proposed model advances the state-of-the-art in the
augmentation of clinical audio data and improves the data bottleneck for the
design of clinical acoustic sensing systems.
