Get Started with Signal Processing Toolbox
Signal Processing Toolbox™ provides functions and apps to manage, 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. You can use the Signal Analyzer app for visualizing and processing signals simultaneously in time, frequency, and time-frequency domains. With the Filter Designer app you can design and analyze FIR and IIR digital filters. Both apps generate MATLAB® scripts to reproduce or automate your work.
Using toolbox functions, you can prepare signal datasets for AI model training by engineering features that reduce dimensionality and improve the quality of signals. You can access and process collections of files and large datasets using signal datastores. With the Signal Labeler app, you can annotate signal attributes, regions, and points of interest to create labeled signal sets. The toolbox supports GPU acceleration in addition to C/C++ and CUDA® code generation for desktop prototyping and embedded system deployment.
Tutorials
- 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. - 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 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. - 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. - 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. - 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.
Analyze Signals
Preprocess Signals
Find Patterns and Extract Features
Design, Analyze, and Apply Digital Filters
Perform Spectral and Time-Frequency Analysis
Apply Signal Processing to Machine Learning and Deep Learning
Featured Examples
Interactive Learning
Signal Processing Onramp
This free, two-hour tutorial provides an interactive introduction to practical signal
processing methods for spectral analysis.
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.
Signal Analysis Made Easy with the Signal Analyzer App
Learn to perform signal analysis tasks in MATLAB with the Signal Analyzer app.
Introduction to Signal Processing Apps in MATLAB
Use Signal Analyzer to import, visualize, preprocess, and analyze
an electrocardiogram signal.