MATLAB and Simulink for Signal Processing

Analyze signals and time-series data. Model, design, and simulate signal processing systems.

Signal processing engineers use MATLAB and Simulink at all stages of development—from analyzing signals and exploring algorithms to evaluating design implementation tradeoffs for building real-time signal processing systems.

Solutions in Action

Access built-in functions and apps for analysis and preprocessing of time-series data, spectral and time-frequency analysis, and signal measurements.

Use apps and algorithms to design, analyze, and implement digital filters from basic FIR and IIR filters to adaptive, multirate, and multistage designs.

Model and simulate signal processing systems with a combination of functions and block diagrams.

Model fixed-point behavior and automatically generate C/C++ or HDL code for deploying on embedded processors, FPGAs, and ASICs.

Develop predictive models on signals and sensor data using machine learning and deep learning workflows.

Get Started

Signal Processing Onramp

Explore more getting started resources

Signal Processing Resources

Video

Getting Started with Simulink for Signal Processing

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Video

Tracking the DNA of Sound with MATLAB

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Video

Digital Filtering Design and Implementation in Simulink

Watch video (9:09)

Key Features

Signal Analysis and Measurements

Visualize and preprocess signals in time, frequency, and time-frequency domains without manually writing code. Characterize signals and signal processing systems using domain-specific algorithms for applications like communications, radar, audio, medical devices, and IoT.

Screenshot of signal analyzer with Display tab open, showing four different graphs. — Analyze signals with built-in app to identify patterns and trends.

Filter Design and Analysis

Design and analyze digital filters from basic lowpass/highpass to advanced FIR/IIR, including multirate, multistage, and adaptive types. Visualize magnitude, phase, and impulse response. Evaluate performance, stability, and phase linearity.

Graph of filtered waveforms in multiple colors. — Analyze digitally filtered waveforms.

Model-Based Design for Signal Processing

Design signal processing systems using block diagrams. Apply Model-Based Design with Simulink for modeling, simulation, verification, and code generation. Use block libraries for specific algorithms and visualize live signals with virtual scopes.

Diagram of a demo using internet low bit rate codec. — Apply Model-Based Design for signal processing applications.

Embedded Code Generation

Generate C/C++ code from signal processing algorithms using MATLAB Coder and Simulink Coder for simulation, prototyping, and embedded use. Create optimized C code for ARM^® Cortex^® processors. Produce Verilog^® and VHDL^® code for FPGA or ASIC design from MATLAB and Simulink models.

Screenshot of a code generation report. — Automatically generate C/C++ code, including code generation reports.

Machine Learning and Deep Learning

Build predictive models for signal processing applications with MATLAB. Exploit built-in signal processing algorithms to extract features for machine learning systems. Work with large datasets for ingesting, augmenting, and annotating signals when developing deep learning applications.