Continuous Wavelet Transform

The continuous wavelet transform (CWT) is a signal processing technique used to analyze data across multiple scales and frequencies, revealing detailed time-frequency information often missed by other methods. Current research focuses on integrating CWT with various machine learning architectures, such as convolutional neural networks (CNNs) and transformers, for applications ranging from speech synthesis enhancement and time series classification to image denoising and biomedical signal analysis (e.g., ECG classification). These advancements demonstrate CWT's effectiveness in improving model accuracy, efficiency, and robustness across diverse fields, leading to significant improvements in performance compared to traditional methods. The resulting enhanced analytical capabilities have broad implications for various scientific disciplines and practical applications.