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：本研究探讨了自动化深度学习在提高鸟类鸣声多类分类准确性和效率方面的潜力，并与传统人工设计的深度学习模型进行了对比。利用西地中海湿地鸟类数据集，我们研究了AutoKeras这一自动化机器学习框架在神经架构搜索与超参数调优中的应用。对比分析验证了我们的假设：AutoKeras生成的模型始终优于MobileNet、ResNet50和VGG16等传统模型。我们的方法和结果凸显了自动化深度学习在推动生物声学研究与模型变革方面的潜力。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需求。本研究阐明了采样、评估和报告的最佳实践，以提升这一新兴领域的可重复性。所有使用代码均公开于 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；对比分析；生物声学；验证数据集；多类分类；声谱图。