This course introduces students to the basic concepts and applications of time-frequency analysis in signal processing as well as their use in pattern recognition. Students will learn, first, to analyze signals in both time and frequency domains via time-frequency methods like Hilbert Transform, Wigner-Ville Distribution (WVD), Short-Time Fourier Transform (STFT), Wavelet Transform (WT), and Empirical Mode Decomposition (EMD). Then, Students will use the extracted time-frequency information as input to pattern recognition approaches, like supervised (classification and regression) and unsupervised learning (clustering).
The course includes both theoretical understanding and practical Python exercises to implement and visualize these techniques.