Get Started with Signal Processing Toolbox

Perform signal processing and analysis

Signal Processing Toolbox™ provides functions and apps to analyze, preprocess, and extract features from uniformly and nonuniformly sampled signals. The toolbox includes tools for filter design and analysis, resampling, smoothing, detrending, and power spectrum estimation. The toolbox also provides functionality for extracting features like changepoints and envelopes, finding peaks and signal patterns, quantifying signal similarities, and performing measurements such as SNR and distortion. You can also perform modal and order analysis of vibration signals.

With the Signal Analyzer app you can preprocess and analyze multiple signals simultaneously in time, frequency, and time-frequency domains without writing code; explore long signals; and extract regions of interest. With the Filter Designer app you can design and analyze digital filters by choosing from a variety of algorithms and responses. Both apps generate MATLAB^® code.

Tutorials

Analyze Signals

Using Signal Analyzer App

Visualize, measure, analyze, and compare signals in the time, frequency, and time-frequency domains.

Extract Voices from Music Signal

Use Signal Analyzer to extract voices from a song by duplicating and filtering signals.

Preprocess Signals

Align Signals with Different Start Times

Synchronize data collected by different sensors at different instants.

Find a Signal in a Measurement

Determine if a signal matches a segment of a noisy longer stream of data.

Find Patterns and Extract Features

Find Peaks in Data

Locate the local maxima in a set of data and determine if those peaks occur periodically.

Extract Features of a Clock Signal

Determine how often and how sharply a bilevel signal turns on and off.

Design, Analyze, and Apply Digital Filters

Filtering Data With Signal Processing Toolbox Software

Design and implement a filter using command-line functions or an interactive app.

Take Derivatives of a Signal

Use a differentiator filter to differentiate a signal without amplifying the noise.

Perform Spectral and Time-Frequency Analysis

Find Periodicity Using Frequency Analysis

Spectral analysis helps characterize oscillatory behavior in data and measure the different cycles.

Find and Track Ridges Using Reassigned Spectrogram

Use the reassigned spectrogram in Signal Analyzer to sharpen the time and frequency localization of spectrograms.

Apply Signal Processing to Machine Learning and Deep Learning

Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram data using deep learning and signal processing.

Waveform Segmentation Using Deep Learning

Segment human electrocardiogram signals using time-frequency analysis and deep learning.

Label Signal Attributes, Regions of Interest, and Points

Use Signal Labeler to label attributes, regions. and points of interest in a set of whale songs.

Featured Examples

Process a Signal with Missing Samples

Signals often have missing samples. To provide estimates for these values, use resampling.

Open Live Script

Find Periodicity Using Autocorrelation

Verify the presence of cycles in a noisy signal, and determine their durations.

Open Live Script

Practical Introduction to Frequency-Domain Analysis

Perform and interpret basic frequency-domain signal analysis using simulated and real data.

Open Script

Practical Introduction to Time-Frequency Analysis

Perform and interpret basic time-frequency signal analysis of nonstationary signals.

Open Live Script

Practical Introduction to Digital Filter Design

Use the designfilt function to design FIR and IIR filters based on frequency response specifications.

Open Live Script

Practical Introduction to Digital Filtering

Design, analyze, and apply digital filters to remove unwanted content from a signal without distorting the data.

Open Live Script

Videos

What Is Signal Processing Toolbox?
Perform signal processing, signal analysis, and algorithm development using Signal Processing Toolbox.

Signal Processing for Machine Learning
This video presents a classification system able to identify the physical activity of a human subject based on smartphone-generated accelerometer signals.