AFRL/RI is involved in all aspect of R&D of acoustical analysis and processing capabilities, to address the needs and requirements that are unique to the DoD’s counter unmanned aerial systems (cUAS).  The group is dedicated to the detection of these systems by exploiting their acoustic emissions. Therefore, a wide spectrum of topics of basic research need to be addressed, such as real-time implementation of acoustic detecting, tracking, and classification of specific acoustical signatures in dynamic environments via array processing. Additional topics include significant thrusts in noise estimation and removal (both spectral and spatial via beamforming), acoustical identification (AID) (both open and close set), acoustic modeling and robust feature extraction. State of the art techniques such as Deep Neural Networks and other machine learning algorithms are used to pursue solutions for real-time and offline problems such as detection and AID.