Transforms and Spectral Analysis
The frequency-domain representation of a signal reveals important signal
characteristics that are difficult to analyze in the time domain. Spectral
analysis lets you characterize the frequency content of a signal. The FFT
and IFFT System objects and blocks in DSP System Toolbox™ enable you to convert a streaming time-domain signal into the
frequency-domain, and vice versa. To compute the spectral estimate of the
signal, use the
System object™ in MATLAB® and the Spectrum Estimator block in
Simulink®. You can visualize the spectral estimate using the Spectrum
Analyzer object and block.
The Spectrum Analyzer in DSP System Toolbox uses the Welch's method of averaging modified periodogram and the filter bank method. Both these methods are FFT-based spectral estimation methods that make no assumptions about the input data and can be used with any kind of signal. For more information on the algorithm the Spectrum Analyzer uses, see Spectral Analysis. To learn how to estimate the power spectral density of a streaming signal in MATLAB, see Estimate the Power Spectrum in MATLAB.
Fourier, Cosine, and Wavelet transforms
- Linear Prediction
Convert between linear predictive coefficients (LPC) and cepstral coefficients, LSF, LSP, and RC
- Spectral Analysis
Parametric and nonparametric methods