Acoustic Sensing

Acoustic sensing utilizes sound waves to gather information about the environment, aiming to extract meaningful data from complex acoustic signals. Current research emphasizes developing robust and efficient machine learning models, including convolutional and recurrent neural networks, often combined with signal processing techniques like source separation and denoising algorithms, to improve accuracy and reduce computational demands in various applications. This field is significant for its diverse applications, ranging from environmental monitoring (e.g., biodiversity assessment, traffic analysis) and healthcare (e.g., respiration monitoring, breach detection during surgery) to robotics (e.g., underwater localization, hand pose tracking). The development of efficient and privacy-preserving methods is a key focus.