Andrea Mor

Papers

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About

Andrea Mor is a researcher at the forefront of industrial acoustics and machine learning, with a focused expertise in sound source localization for smart manufacturing environments. Her most impactful work introduces a novel application of Convolutional Long Short-Term Memory (ConvLSTM) networks to pinpoint noise sources in complex, real-world factory settings—a critical challenge for worker safety and automated quality control. This flagship paper, published in 2024 and already garnering 7 citations, demonstrates her ability to bridge deep learning with practical industrial engineering. Mor’s contributions are particularly notable for addressing the limitations of traditional beamforming and microphone array techniques in noisy, reverberant workspaces, offering a robust, data-driven solution that improves localization accuracy. Her work has immediate implications for reducing occupational hearing loss and enhancing predictive maintenance in Industry 4.0. As a rising voice in the field, Andrea Mor is establishing a research trajectory that promises to transform how we monitor and manage acoustic environments in the factories of the future.