Auditory Filterbanks

Auditory filterbanks mimic the human auditory system's frequency analysis, aiming to efficiently represent audio signals for various applications like speech processing and sound classification. Current research focuses on developing more flexible and computationally efficient filterbank designs, including exploring rational-exponent filters and multiresolution neural networks to improve upon existing models like Gammatone and LEAF filterbanks. These advancements are driven by the need for improved accuracy and reduced computational cost in tasks such as speaker localization and audio classification, impacting fields ranging from robotics to machine learning.