Signal Processing Toolbox

Perform signal processing and analysis

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 to visualize and process signals simultaneously in time, frequency, and time-frequency domains. With the Filter Designer app, you can design and analyze FIR and IIR digital filters.

Using toolbox functions and the Signal Feature Extractor app, you can prepare signal datasets for AI model training by engineering features that reduce dimensionality and improve the quality of signals. With the Signal Labeler app, you can annotate signals in time and time-frequency domains to create labeled signal sets for training AI models. The toolbox supports GPU acceleration in addition to C/C++ and CUDA^® code generation for desktop prototyping and embedded system deployment.

Signals plotted in time and time-frequency domains with corresponding labels within the Signal Labeler app.

Machine Learning and Deep Learning for Signals

Perform preprocessing, feature engineering, signal labeling, and dataset generation for machine learning and deep learning workflows. Use the Signal Labeler app to create ground truth datasets and the Signal Feature Extractor app to extract features for model training.

Labeling Signals for AI Tasks with Signal Labeler App (3:33)

Documentation | Examples

Signals plotted in time, frequency, and time-frequency domains in the Signal Analyzer app.

Signal Exploration and Preprocessing

Visualize, preprocess, and explore signals using the Signal Analyzer app. Denoise, smooth, and detrend signals to prepare them for further analysis.

Signal Analysis Made Easy with the Signal Analyzer App (4:28)

Documentation | Examples

Time-domain features extracted and displayed with the Signal Feature Extractor app.

Feature Extraction and Signal Measurement

Measure and extract signal features, including peaks, power, bandwidth, and distortion. Compute signal statistics and metrics related to pulses and transitions. Extract features for an entire dataset using the Signal Feature Extractor app.

Performing Peak Detection (4:27)

Documentation | Examples

Filter Designer app used to design and compare a range of filters, including low-pass, high-pass, bandpass, and bandstop filters.

Filter Design and Analysis

Design, analyze, and implement digital filters. Use the Filter Designer app and Filter Analyzer app to design and analyze a variety of digital FIR, IIR, and multirate filters, such as low-pass, high-pass, bandpass, and bandstop filters.

Documentation | Examples

Power spectral density plot showing the 3-dB bandwidth of two signals.

Spectral Analysis

Characterize the frequency content of a signal using spectral estimation, including parametric and subspace methods. Design, visualize, and implement windowing functions.

Documentation | Examples

STFT plotted as a waterfall plot of a voltage-controlled oscillator output, controlled by a sinusoid sampled at 10 kHz.

Time-Frequency Analysis

Visualize and compare the time-frequency content of nonstationary signals using methods such as spectrogram analysis, synchrosqueezing, and reassignment.

Documentation | Examples

Waterfall plot of an order-RPM map with gear and pinion graphics next to the plot.

Vibration Analysis

Characterize vibrations in mechanical systems. Use order analysis to extract and visualize spectral content occurring in rotating machinery. Perform experimental modal and fatigue analyses.

Documentation | Examples

Workflow of C code generation from MATLAB to generated code to processor hardware.

GPU Acceleration and Code Generation

Accelerate the execution of your signal processing algorithms using GPUs. Generate portable C/C++ source code, standalone executables, or standalone applications from your MATLAB code.

Documentation | Examples

Product Resources:

Documentation Examples Videos Requirements Release notes

Developing and Deploying Sonar and Echosounder Data Analysis Software

“MATLAB proved to be an ideal environment for developing SonarScope because it enabled me to develop algorithms, visualize results, and then refine the algorithms in an iterative cycle.”

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