Analyze signals using sparse representation in the wavelet domain.

Wavelet transforms are a mathematical means for performing signal analysis when signal frequency varies over time. For certain classes of signals and images, wavelet analysis provides more precise information about signal data than other signal analysis techniques.

Common applications of wavelet transforms include:

  • Speech and audio processing
  • Image and video processing
  • Biomedical imaging
  • 1D and 2D applications in communications and geophysics

You can perform wavelet analysis in MATLAB® and Wavelet Toolbox™, which lets you compute wavelet transform coefficients. The toolbox includes many wavelet transforms that use wavelet frame representations, such as continuous, discrete, nondecimated, and stationary wavelet transforms. These products can be used for image compression, feature extraction, signal denoising, data compression, and time-series analysis.

See also: Signal Processing Toolbox, DSP System Toolbox, wavelet transforms videos