Short Time Fourier Transform

The Short-Time Fourier Transform (STFT) is a fundamental signal processing technique that decomposes signals into time-frequency representations, revealing how frequency content changes over time. Current research focuses on improving STFT's application in various fields by addressing limitations like non-uniform phase distributions and developing differentiable versions for gradient-based optimization within deep learning models, such as convolutional neural networks (CNNs) and transformers. These advancements are significantly impacting diverse applications, including speech enhancement, machine condition monitoring, and audio source separation, by enabling more accurate feature extraction and improved model performance. The development of efficient and robust STFT-based methods continues to be a key area of investigation.