Frequency Aware Convolution

Frequency-aware convolution aims to improve convolutional neural networks (CNNs) by incorporating frequency domain information, addressing limitations of standard CNNs in handling data with frequency-dependent patterns, such as audio and time series. Current research focuses on developing efficient architectures that integrate time and frequency domain representations, often employing techniques like Discrete Cosine Transform (DCT) and incorporating frequency-specific attention mechanisms or dynamic convolutions. These advancements enhance feature extraction and improve performance in applications like sound event detection, time series forecasting, and image processing, particularly for tasks involving small objects or intricate patterns.