Transforms, Correlation, and Modeling

Cross-correlation, autocorrelation, Fourier, DCT, Hilbert, Goertzel, parametric modeling, linear predictive coding

Signal Processing Toolbox™ provides functions that let you compute correlation, convolution, and transforms of signals. Use the fast Fourier transform to decompose your data into frequency components. Filter signals by convolving them with transfer functions. Use correlation to quantify signal similarities. Use the discrete cosine transform to compress data.

Highlighted Topics

Categories

Transforms
Fourier, chirp Z, DCT, Hilbert, cepstrum, Walsh-Hadamard
Correlation and Convolution
Cross-correlation, autocorrelation, cross-covariance, autocovariance, linear and circular convolution
Signal Modeling
Linear prediction, autoregressive (AR) models, Yule-Walker, Levinson-Durbin

Featured Examples

Accelerating Correlation with GPUs

Accelerating Correlation with GPUs

Use a graphics processing unit to accelerate cross-correlation in applications that involve large data sets.

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DFT Estimation with the Goertzel Algorithm

DFT Estimation with the Goertzel Algorithm

Use the Goertzel method to implement a DFT-based Dual-Tone Multi-Frequency detection algorithm.

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Graph Signal Processing and Brain Signal Analysis

Graph Signal Processing and Brain Signal Analysis

Perform graph signal processing to analyze brain activity by decomposing brain signals into aligned and liberal components.

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Single Sideband Modulation via the Hilbert Transform

Single Sideband Modulation via the Hilbert Transform

Use the discrete Hilbert transform to implement single sideband modulation, an efficient form of AM.

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