Signal Processing Toolbox

Powerful tools for algorithm development, signal and linear system analysis, and time-series data modeling

The Signal Processing Toolbox provides a rich, customizable framework for digital signal processing (DSP). Built on a solid foundation of filter design and spectral analysis techniques, the toolbox contains powerful tools for algorithm development, signal and linear system analysis, and time-series data modeling. The toolbox is useful in applications such as speech and audio processing, communications, geophysics, real-time control, finance, radar, and medicine.

Features

Signal and linear system models
Digital and analog filter design, analysis, and implementation
FFT, DCT, and other transforms
Spectrum estimation and statistical signal processing
Parametric time-series modeling
Waveform generation
Windowing

Highlights

Algorithm Development. The Signal Processing Toolbox is the ideal environment for signal analysis and DSP algorithm development. It uses industry-tested signal processing algorithms that have been carefully chosen and implemented for maximum efficiency and numeric reliability.

Signal and Linear System Models. The Signal Processing Toolbox provides a broad range of models for representing signals and linear time-invariant systems, allowing you to choose the scheme that best suits your application. The toolbox also includes functions for transforming models from one representation to another.

Filter Design. The Signal Processing Toolbox features a full suite of design methods for finite impulse response (FIR) and infinite impulse response (IIR) digital filters. These methods support the rapid design and evaluation of lowpass, highpass, bandpass, bandstop, and multiband filters such as Butterworth, Chebyshev, elliptic, Yule-Walker, window-based, least-squares, and Parks-McClellan.

Low pass filter design
Bandpass filter design

The Filter Design GUI provides point and click filter design capability to meet frequency domain attenuation requirements. The new FIR filter design methods include:

The Complex Chebyshev Approximation Method for designing FIR filters with non-linear phase, complex coefficients, or arbitrary responses (cremez). This algorithm was developed by McClellan and Karam in 1995.
The Constrained Least Squares Method allows the user to explicitly control the maximum error (ripple) without introducing an arbitrary data window (as in fir1 and fir2) or transition band (as in firls or remez).
The Order Estimation method calculates the minimum filter order required for filters designed with a Kaiser window (kaiserord).
The new IIR filter design method is the generalized Butterworth Method for lowpass filters which are maximally flat in the passband and stopband (maxflat).

Spectral Analysis. Based on a highly optimized FFT, the Signal Processing Toolbox has unsurpassed facilities for frequency-domain analysis and spectral estimation. The toolbox includes functions for computing the discrete Fourier transform, discrete cosine transform, Hilbert transform, and other transforms useful in analysis, coding, and filtering. The spectral analysis methods available include Welch's method, the Maximum Entropy method, The Multitaper method, and the MUSIC (MUltiple Signal Classification) method.

Visualization. With the new GUIs in the toolbox, you can interactively view and measure signals, design and apply filters, and perform spectral analysis while exploring the effect of different analysis parameters and methods. The GUIs are extrememly helpful for visualizing time series, spectra, time-frequency information, and pole-zero locations.

Additional Applications. The Signal Processing Toolbox is a foundation for numerous other application solutions. For example, you can combine it with the Image Processing Toolbox to manipulate and analyze 2-D signals and image data. You can also pair it with the System Identification Toolbox to perform time-domain parametric modeling. Or you can use it with the Neural Network and Fuzzy Logic Toolboxes to create a set of tools for preprocessing data or extracting features for classification. The Signal Generation tool can produce several standard signals, including the rectangular pulse, triangular pulse, Gaussian pulse, chirp, and impulse train.

Complementing to many application areas, the Signal Processing Toolbox enhances your ability to investigate new research ideas and design custom solutions to complex problems.

Stefan Steinhaus, webmaster@steinhaus-net.de