Acoustic Signal Processing

Acoustic signal processing focuses on extracting meaningful information from sound waves, aiming to improve signal quality, identify sources, and understand the underlying acoustic phenomena. Current research emphasizes the application of machine learning, particularly deep learning architectures like DenseNet and recurrent neural networks with attention mechanisms, to enhance tasks such as noise reduction, source localization, and fault detection in diverse applications. These advancements leverage techniques like spherical harmonics decomposition and advanced beamforming to achieve higher accuracy and efficiency, impacting fields ranging from industrial monitoring and medical diagnostics to spatial audio and environmental sensing. The development of more robust and computationally efficient algorithms continues to be a major focus.