Frequency Perception

Frequency perception research explores how information encoded in the frequency domain of signals (e.g., sound, images) is processed and utilized. Current research focuses on developing novel neural network architectures, such as time-frequency perceivers and frequency-augmented variational autoencoders, to effectively leverage both time and frequency domain representations for tasks like time series forecasting, camouflaged object detection, and music transcription. These advancements improve the accuracy and efficiency of signal processing in various applications, impacting fields ranging from audio processing and image reconstruction to medical imaging and machine learning. The ability to effectively analyze and synthesize frequency information is crucial for enhancing the performance of numerous technologies.