Time-Frequency Analysis Toolbox

This document introduces a specialized toolbox designed for time-frequency analysis applications. The toolbox contains a collection of signal processing functions, with wavelet transform functions serving as the core component. Wavelet transform represents a powerful signal processing technique that enables simultaneous analysis in both time and frequency domains, revealing crucial time-frequency characteristics within signals. The implementation includes continuous wavelet transform (CWT) and discrete wavelet transform (DWT) algorithms, featuring configurable mother wavelets like Morlet, Daubechies, and Coiflet families. Through utilizing the wavelet transform functions in this toolbox, researchers can achieve deeper insight and more effective processing of various signal types, including audio signals, image data, and biomedical signals. Key functions include wavelet coefficient computation, scale-to-frequency conversion, and inverse wavelet reconstruction with proper boundary handling. The toolbox therefore provides essential utilities for engineers and researchers engaged in time-frequency analysis and advanced signal processing tasks, offering both standard implementations and customizable parameters for specialized applications.