Filter Bank

Filter banks are collections of band-pass filters used to decompose signals into different frequency components, facilitating analysis and feature extraction. Current research emphasizes learning filter bank parameters directly from data, rather than relying solely on handcrafted designs, often employing convolutional neural networks or transformer architectures for this purpose. This shift towards learned filter banks improves performance in applications like speech recognition, object detection, and keyword spotting, particularly in resource-constrained environments or when dealing with noisy data, by enabling adaptation to specific signal characteristics and reducing computational demands. The resulting optimized representations enhance the accuracy and efficiency of various signal processing and machine learning tasks.