Signal Processing Toolbox™ provides a family of spectral analysis functions and apps that let you characterize the frequency content of a signal. FFT-based nonparametric methods, such as Welch's method or the periodogram, make no assumptions about the input data and can be used with any kind of signal. Parametric and subspace methods, such as Burg's, covariance, and MUSIC, incorporate prior knowledge of the signal and can yield more accurate spectral estimates.
Compute power spectra of nonuniformly sampled signals or signals with missing samples using the Lomb-Scargle method. Analyze nonstationary signals using time-frequency techniques like the spectrogram and measure signal similarities in the frequency domain by estimating their spectral coherence. Design and analyze Hamming, Kaiser, Gaussian, and other data windows.