Signal Processing Toolbox™ provides industry-standard algorithms for analog and digital signal processing (DSP).   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   Generating, Visualizing, and Analyzing Signals Signal Processing Toolbox enables you to generate and analyze discrete signals in MATLAB®. You can: ▪ Create vectors of discrete signal values ▪ Generate standard waveforms using built-in toolbox functions ▪ Import signals from files ▪ Acquire signals from instruments, multimedia devices, and other hardware Generating Waveforms You can generate continuous and discrete signals using signal generation functions in the toolbox. Support for commonly used waveforms includes: ▪ Periodic waveforms, such as sine, square, sawtooth, and rectangular signals ▪ Aperiodic waveforms, such as chirp and Gaussian pulse signals ▪ Common sequences, such as unit impulse, unit step, and unit ramp   Interactive Signal Processing Signal Processing Tool (SPTool) is an interactive tool that enables basic signal analysis tasks. From the SPTool interface, you can launch other tools, including Signal Browser, Filter Design and Analysis Tool (FDATool), and Spectrum Viewer. Using these tools, you can: ▪ Import and visualize single-channel or multichannel signals in the time domain ▪ Make signal measurements, such as slope and peak value ▪ Play audio signals on a PC sound card ▪ Design or import FIR and IIR filters of various lengths and response types ▪ View characteristics of a designed or imported filter, including magnitude, phase, impulse, and step responses ▪ Apply the filter to a selected signal ▪ Graphically analyze signals in the frequency domain using a variety of spectral estimation methods   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 http://www.mathworks.com/products/datasheets/pdf/signal-processing-toolbox.pdf