| dc.description.abstract | Infant cries, akin to adult speech, serve as a means for infants to express their needs
and feelings, allowing caregivers to receive cues and provide corresponding care.
However, there lacks a comprehensive dataset of infant cries, leading to suboptimal
performance in using deep learning models to predict infant needs based on cry sounds.
This study aims to explore the improvement of infant cry classification models
using Generative Adversarial Networks （GANs）. Due to the limited availability of
infant cry datasets, the predictive performance of deep learning classification models is
compromised. To address this issue, we propose using GANs to generate additional
infant cry samples to augment the training dataset and subsequently enhance the
predictive performance of the classification model.
In this study, we collected samples of real infant cries corresponding to five
different needs （anger, hunger, insecured, poopee, sleepy） in advance. We then
utilized the WaveGAN to generate additional infant cry samples for each need category.
The generated samples were combined with the original dataset to form a new
augmented dataset. Subsequently, this augmented dataset was used for model training,
while the original dataset was also separately utilized for training. The performances of
models trained on the augmented dataset and the original dataset were compared
individually. We employed Long Short-Term Memory （LSTM） deep learning models
for the classification of infant cry needs.
iii
The experimental results demonstrate that the model trained on the augmented
dataset, which incorporates the generated data, significantly outperforms the model
trained solely on the original dataset. This indicates that GANs effectively augment the
training dataset, thereby improving the model′s generalization ability and accuracy. In
conclusion, we believe that using GANs to generate additional infant cry samples is an
effective approach to enhance the predictive performance of infant cry classification
models. This contributes significantly to improving the standards and efficiency of
infant healthcare. | en_US |