TITLE: Conditional Generative Data Augmentation for Clinical Audio Datasets 

ABSTRACT: In this work, we propose a novel data augmentation method for clinical audio
datasets based on a conditional Wasserstein Generative Adversarial Network with
Gradient Penalty (cWGAN-GP), operating on log-mel spectrograms. To validate our
method, we created a clinical audio dataset which was recorded in a real-world
operating room during Total Hip Arthroplasty (THA) procedures and contains
typical sounds which resemble the different phases of the intervention. We
demonstrate the capability of the proposed method to generate realistic
class-conditioned samples from the dataset distribution and show that training
with the generated augmented samples outperforms classical audio augmentation
methods in terms of classification performance. The performance was evaluated
using a ResNet-18 classifier which shows a mean Macro F1-score improvement of
1.70% in a 5-fold cross validation experiment using the proposed augmentation
method. Because clinical data is often expensive to acquire, the development of
realistic and high-quality data augmentation methods is crucial to improve the
robustness and generalization capabilities of learning-based algorithms which
is especially important for safety-critical medical applications. Therefore,
the proposed data augmentation method is an important step towards improving
the data bottleneck for clinical audio-based machine learning systems.
In this work, we propose a novel data augmentation method for clinical audio
datasets based on a conditional Wasserstein Generative Adversarial Network with
Gradient Penalty (cWGAN-GP), operating on log-mel spectrograms. To validate our
method, we created a clinical audio dataset which was recorded in a real-world
operating room during Total Hip Arthroplasty (THA) procedures and contains
typical sounds which resemble the different phases of the intervention. We
demonstrate the capability of the proposed method to generate realistic
class-conditioned samples from the dataset distribution and show that training
with the generated augmented samples outperforms classical audio augmentation
methods in terms of classification performance. The performance was evaluated
using a ResNet-18 classifier which shows a mean Macro F1-score improvement of
1.70% in a 5-fold cross validation experiment using the proposed augmentation
method. Because clinical data is often expensive to acquire, the development of
realistic and high-quality data augmentation methods is crucial to improve the
robustness and generalization capabilities of learning-based algorithms which
is especially important for safety-critical medical applications. Therefore,
the proposed data augmentation method is an important step towards improving
the data bottleneck for clinical audio-based machine learning systems.
Conditional Generative Data Augmentation for Clinical Audio Datasets
http://arxiv.org/abs/2203.11570v3