Development and evaluation of automated waveform analysis techniques for BEGe detectors within the VIP project. Two approaches were tested: feature-based machine learning and deep learning directly on raw signals. A custom CDAE–FAE–GMVAE neural architecture achieved up to 98% accuracy, enabling efficient pulse discrimination even with minimal labeled data. Download the [Slides](/files/RisultatiINFN.pptx)