  This study investigates the potential of automated deep learning to enhance
the accuracy and efficiency of multi-class classification of bird
vocalizations, compared against traditional manually-designed deep learning
models. Using the Western Mediterranean Wetland Birds dataset, we investigated
the use of AutoKeras, an automated machine learning framework, to automate
neural architecture search and hyperparameter tuning. Comparative analysis
validates our hypothesis that the AutoKeras-derived model consistently
outperforms traditional models like MobileNet, ResNet50 and VGG16. Our approach
and findings underscore the transformative potential of automated deep learning
for advancing bioacoustics research and models. In fact, the automated
techniques eliminate the need for manual feature engineering and model design
while improving performance. This study illuminates best practices in sampling,
evaluation and reporting to enhance reproducibility in this nascent field. All
the code used is available at https:
//github.com/giuliotosato/AutoKeras-bioacustic
  Keywords: AutoKeras; automated deep learning; audio classification; Wetlands
Bird dataset; comparative analysis; bioacoustics; validation dataset;
multi-class classification; spectrograms.
