Conditional Generative Data Augmentation for Clinical Audio Datasets
- URL: http://arxiv.org/abs/2203.11570v1
- Date: Tue, 22 Mar 2022 09:47:31 GMT
- Title: Conditional Generative Data Augmentation for Clinical Audio Datasets
- Authors: Matthias Seibold, Armando Hoch, Mazda Farshad, Nassir Navab, Philipp
F\"urnstahl
- Abstract summary: We propose a novel data augmentation method for clinical audio datasets based on a conditional Wasserstein Generative Adversarial Network with Gradient Penalty.
To validate our method, we created a clinical audio dataset which was recorded in a real-world operating room during Total Hipplasty (THA) procedures.
We show that training with the generated augmented samples outperforms classical audio augmentation methods in terms of classification accuracy.
- Score: 36.45569352490318
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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 accuracy. The performance was evaluated
using a ResNet-18 classifier which shows a mean per-class accuracy improvement
of 1.51% 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. The code and dataset will be published upon acceptance.
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