Key Features

  • Signal Analyzer app for visualizing and comparing signals simultaneously in time, frequency, and time-frequency domains
  • FIR and IIR filter design and analysis
  • Algorithms for finding signal similarities, envelopes, patterns, changepoints, peaks, and outliers
  • Measurements such as transition and pulse metrics, band power, bandwidth, and distortion
  • Power spectrum estimation of uniformly and nonuniformly sampled data
  • Order analysis of vibration signals and modal analysis of mechanical systems

Signal Exploration

Signal Processing Toolbox™ provides apps and functions that let you analyze, visualize, and compare multiple signals and detect and extract features or interesting events. For example, with the Signal Analyzer app, you can:

  • Analyze signals in time, frequency, and time-frequency domains
  • Preprocess signals to enhance signal quality
  • Extract regions of interest from signals
Visualize and compare multiple signals and spectra.
Measure time delay between correlated signals.
Identify tones by adjusting the window leakage parameter using the Signal Analyzer app.

Signal Preprocessing

Signal Processing Toolbox provides functions that let you detect outliers, smooth and work with irregularly sampled signals, and prepare them for further analysis. For example, you can:

  • Remove noise, outliers, and spurious content from data
  • Enhance signals, visualize signals, and discover patterns
  • Change the sample rate of a signal or make the sample rate constant for irregularly sampled signals or signals with missing data
Change the sample rate of a signal without introducing artifacts.
Denoise signals with weighted moving average and Savitzky-Golay filters.

Remove spikes with median filters.
Generate pulses and swept-frequency signals (chirps, VCOs).
Interpolate missing segments of signals using autoregressive modeling.
Measure delays and align signals using cross-correlation.

Feature Extraction and Signal Measurements

Signal Processing Toolbox provides functions that let you explore and extract patterns in signals. Specifically, you can:

  • Locate signal peaks and determine their height, width, and distance to neighbors
  • Find changepoints in signals and align signals using dynamic time warping 
Find peak locations and measure peak height, prominence, and width.
Extract features for gait signal classification.
Find exact or closely matching patterns in signals.
Detect abrupt changes or interesting events in time-series data.
Extract the envelope of a signal using the Hilbert transform and the analytic signal.
Measure signal-to-noise ratio (SNR), total harmonic distortion (THD), and signal-to-noise and distortion ratio (SINAD).
Measure band power, bandwidth, and mean and median frequencies.

Digital and Analog Filters


Digital Filters

Use the functions and apps within Signal Processing Toolbox to design, analyze, and implement a variety of digital FIR and IIR filters, such as lowpass, highpass, and bandstop. With these functions and apps, you can:

  • Visualize magnitude, phase, group delay, impulse, and step responses
  • Examine filter poles and zeros
  • Evaluate filter performance by testing stability and phase linearity
  • Apply filters to data and remove delays and phase distortion using zero-phase filtering
Compensate for delay and distortion introduced by filters.
Explore lowpass, highpass, bandpass, bandstop, differentiator, and arbitrary-magnitude frequency responses.
Specify different filter design constraints, and compare FIR design algorithms such as Parks-McClellan (equiripple), least-squares, and Kaiser window.
Compare magnitude and group delay responses of Butterworth, Chebyshev, and elliptic IIR filters.

Analog Filters

Signal Processing Toolbox provides functions for analog filter design and analysis. Supported analog filter types include Butterworth, Chebyshev, Bessel, and elliptic. The toolbox also contains discretization functions, such as the impulse invariance and bilinear transformation methods for analog-to-digital filter conversion.


Time-Frequency and Spectral Analysis

Characterize the frequency content of a signal using the family of spectral analysis functions and apps within Signal Processing Toolbox. FFT-based nonparametric methods, such as Welch's method or the periodogram, make no assumptions about the input data and can be used with any kind of signal. Parametric and subspace methods, such as Burg's, Yule-Walker, and MUSIC, incorporate prior knowledge of the signal and can yield more accurate spectral estimates. With these functions and apps, you can:

  • Compute power spectra of nonuniformly sampled signals or signals with missing samples using the Lomb-Scargle method
  • Analyze signals using time-frequency techniques such as the spectrogram, and measure signal similarities in the frequency domain by estimating spectral coherence
Use spectrogram to determine when a frequency component is present in a signal, and explore time-frequency resolution tradeoffs.
Estimate spectral coherence between signals, and measure relative phase between correlated frequency components.
Lower PSD bias and variability using windowing and averaging with Welch and multitaper methods.
Estimate spectra of nonuniformly sampled signals or signals with missing samples.
Obtain sharp time-frequency estimates and extract signal modes.
Model short signals as outputs of autoregressive (AR) processes to achieve higher spectral resolution.

Vibration Analysis

Signal Processing Toolbox provides functions that let you study and characterize vibrations in mechanical systems. Specifically, you can:

  • Use order analysis to analyze and visualize spectral content occurring in rotating machinery
  • Track and extract orders and their time-domain waveforms
  • Estimate the average spectrum of a signal as a function of order
  • Perform experimental modal analysis by estimating frequency-response functions, natural frequencies, damping ratios, and mode shapes 
Identify the source of unwanted vibrations using order analysis.
Analyze condition of a gearbox using time-synchronous averaging and envelope spectra.
Analyze dynamic behavior of wind turbine blades by estimating mode shape vectors from frequency-response function estimates.