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.
Connect AI Agents to Signal Processing Toolbox
Bring domain-specific capabilities to your agentic AI workflow.
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.
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.
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.
Spectral Analysis
Characterize the frequency content of a signal using spectral estimation, including parametric and subspace methods. Design, visualize, and implement windowing functions.
Time-Frequency Analysis
Visualize and compare the time-frequency content of nonstationary signals using methods such as spectrogram analysis, synchrosqueezing, and reassignment.
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.
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.
Product Resources:
“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.”
Signal Processing Toolbox FAQs
Signal Processing Toolbox provides functions and apps to manage, analyze, preprocess, and extract features from uniformly and nonuniformly sampled signals, including tools for filter design, resampling, smoothing, detrending, and power spectrum estimation.
The toolbox includes Signal Analyzer for visualizing and processing signals in time, frequency, and time-frequency domains; Filter Designer for designing and analyzing FIR and IIR digital filters; Filter Analyzer for analyzing digital filters; Signal Labeler for annotating signals to create labeled datasets; and Signal Feature Extractor for extracting information from signals to train AI models.
Yes, you can prepare signal datasets for AI model training by engineering features that reduce dimensionality and improve signal quality, and use the Signal Labeler app to create ground truth datasets and extract features to train AI models.
You can design and analyze digital and analog filters using the Filter Designer app or the Design Filter live editor task to create a variety of digital FIR and IIR filters, such as lowpass, highpass, bandpass, and bandstop.
Yes, the toolbox supports GPU acceleration for faster execution and generates portable C/C++ source code and CUDA code generation for desktop prototyping and embedded system deployment.
You can measure and extract distinctive features including peaks, power, bandwidth, distortion, signal statistics, and metrics related to frequency and time-frequency domains with toolbox functions.
Yes, you can characterize vibrations in mechanical systems, use order analysis to analyze spectral content in rotating machinery, and perform experimental modal analysis and fatigue analysis.
Signal Analyzer allows you to visualize, preprocess, and explore signals simultaneously in time, frequency, and time-frequency domains, and perform operations like denoising, smoothing, and detrending to prepare signals for further analysis.
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