Classical Power Spectrum Estimation

This implementation demonstrates classical power spectrum estimation through the averaged periodogram method, featuring comprehensive code annotations. The approach offers high accuracy and reliability, making it widely applicable in signal processing applications. By performing spectral analysis on signals, we can characterize frequency properties and energy distribution patterns, enabling better signal comprehension and processing. Key implementation aspects include: - Segmentation of input signal into overlapping/non-overlapping frames - Application of window functions (e.g., Hamming, Hanning) to reduce spectral leakage - Computation of individual periodograms using FFT algorithms - Averaging across multiple periodograms to reduce variance - Proper normalization and scaling of power spectral density values The algorithm effectively balances frequency resolution and statistical stability through parameter optimization, providing robust spectral estimates for various signal types including stationary and quasi-stationary processes.