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.

翻译：本研究探讨了自动深度学习在提升鸟类声音多类分类准确性与效率方面的潜力，并与传统人工设计的深度学习模型进行对比。基于西地中海湿地鸟类数据集，我们采用自动化机器学习框架AutoKeras实现神经网络架构搜索与超参数调优的自动化。比较分析验证了我们的假设：AutoKeras生成模型在性能上始终优于MobileNet、ResNet50和VGG16等传统模型。我们的方法及研究结果凸显了自动深度学习在推进生物声学研究与模型发展中的变革潜力。事实上，自动化技术在提升性能的同时消除了人工特征工程和模型设计的必要性。本研究阐明了采样、评估与报告的最佳实践，以增强这一新兴领域的可复现性。所有代码均已公开于 https://github.com/giuliotosato/AutoKeras-bioacustic。关键词：AutoKeras；自动深度学习；音频分类；湿地鸟类数据集；比较分析；生物声学；验证数据集；多类分类；声谱图。