Signal Processing Toolbox

Introduction

Signal Processing Toolbox™ provides industry-standard algorithms for analog and digital signal processing (DSP). You can use the toolbox to visualize signals in time and frequency domains, compute FFTs for spectral analysis, design FIR and IIR filters, and implement convolution, modulation, resampling, and other signal processing techniques. Algorithms in the toolbox can be used as a basis for developing custom algorithms for audio and speech processing, instrumentation, and baseband wireless communications.

Key Features

• Signal and linear system models
• Waveform and pulse generation functions, including sine, square, sawtooth, and Gaussian pulse
• Statistical signal processing and data windowing functions
• Power spectral density estimation algorithms, including periodogram, Welch, and Yule-Walker
• Digital FIR and IIR filter design, analysis, and implementation methods
• Analog filter design methods, including Butterworth, Chebyshev, and Bessel
• Signal transforms, including fast Fourier transform (FFT), discrete Fourier transform (DFT), and short-time Fourier transform (STFT)
• Linear prediction and parametric time-series modeling

Analysis and visualization tools for verifying numerical accuracy and performance. Example plots from Signal Processing Toolbox include (clockwise from top left): A periodogram of a numerically controlled oscillator; a reconstructed ECG signal using the Walsh-Hadamard transform shown with the original ECG signal; the magnitude response of a low-pass FIR filter, with a specification mask overlay; and the impulse response of a Gaussian pulse-shaping filter for various bandwidths.