Periodogram Method for Spectral Estimation

This self-implemented periodogram method for spectral estimation may be beneficial for beginners learning signal processing techniques. The algorithm works by decomposing signals into periodic components and representing them as spectral constituents to estimate frequency content. Key implementation aspects include: - Computing the squared magnitude of the discrete Fourier transform (DFT) of windowed signal segments - Handling different signal types through appropriate windowing functions (Hamming, Hanning, etc.) - Supporting both amplitude and power spectrum representations The method can be applied to various signal types such as audio signals, vibration data, and other time-series measurements. For readers seeking deeper understanding, the accompanying reference book provides detailed mathematical foundations and practical implementation considerations. Code structure typically involves: 1. Signal preprocessing and windowing 2. Fast Fourier Transform (FFT) computation 3. Spectral magnitude calculation and normalization 4. Frequency axis scaling based on sampling rate