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Performing Spectral Analysis in MATLAB
Spectral analysis is key to understanding signal characteristics, and it can be applied across all signal types, including radar signals, audio signals, seismic data, financial stock data, and biomedical signals. Signal Processing Toolbox provides MATLAB functions for estimating the power spectral density, mean-square spectrum, pseudo spectrum, and average power of signals.
Algorithms for Spectral Analysis in MATLAB
Spectral estimation algorithms in the toolbox include:
- FFT-based methods, such as periodogram, Welch, and multitaper
- Parametric methods, such as Burg and Yule-Walker
- Eigen-based methods, such as eigenvector and multiple signal classification (MUSIC)
Visualization in the Frequency Domain
Spectral analysis functions in the toolbox enable you to compute and view a signal’s:
- Time-frequency representation of a signal using the spectrogram function
- Power spectral density
- Mean-square spectrum
Visualizing signal spectra obtained with spectral analysis methods in MATLAB. Example plots from Signal Processing Toolbox include (clockwise from top left): Spectrogram of clean and noisy audio signals; mean-square spectrum of A/D converter input and output signals with aliasing in the output; and power spectral density of a noisy 200 Hz cosine signal, with a 95% confidence interval.