Using MathWorks® signal processing products, you can analyze time-series data, develop algorithms, and design and evaluate filters. Our signal processing products provide workflows for audio, deep learning, phased array, and radar applications. To model your applications, you can use a combination of language-based programming and Simulink® block diagrams, and then analyze the behavior of the systems for testing. With MATLAB® Coder™ and GPU Coder™, you can generate code from the signal processing algorithms that you develop and deploy solutions onto hardware.
Products for Signal Processing
- Practical Introduction to Digital Filtering (Signal Processing Toolbox)
Design, analyze, and apply digital filters to remove unwanted content from a signal without distorting the data.
- Multirate Filtering in MATLAB and Simulink (DSP System Toolbox)
Perform multirate filtering using rate conversion objects and blocks.
- Compare Speed Performance in Frame-Based Processing Mode Using Simulink Profiler (DSP System Toolbox)
Process signals using frame-based processing and compare the performance using Simulink profiler.
- Measure Signal Similarities (Signal Processing Toolbox)
Compare signals with different sample rates or frequency content, measure delays, and determine if there is a signal in a measurement.
- Measure and Manage Impulse Responses (Audio Toolbox)
Capture and analyze impulse response (IR) measurements by using the Impulse Response Measurer.
- Using Signal Analyzer App (Signal Processing Toolbox)
Visualize, measure, analyze, and compare signals in the time, frequency, and time-frequency domains.
- Signal Visualization and Measurements in MATLAB (DSP System Toolbox)
Visualize and measure signals in the time and frequency domain in MATLAB® using a time scope and spectrum analyzer.
Spectral, Time-Frequency, and Multiresolution Analysis
- Time-Frequency Gallery (Signal Processing Toolbox)
Examine the features and limitations of the time-frequency analysis functions provided by Signal Processing Toolbox™.
- Practical Introduction to Continuous Wavelet Analysis (Wavelet Toolbox)
Perform and interpret continuous wavelet analysis.
- Estimate the Power Spectrum in Simulink (DSP System Toolbox)
Compute the power spectrum using the Spectrum Analyzer and the Spectrum Estimator blocks.
Machine Learning and Deep Learning
- Detect Anomalies Using Wavelet Scattering with Autoencoders (Wavelet Toolbox)
Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.
- Deep Learning for Audio Applications (Audio Toolbox)
Learn common tools and workflows to apply deep learning to audio applications.
- Label Radar Signals with Signal Labeler (Radar Toolbox)
Label the time and frequency features of pulse radar signals with added noise.
- Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
Modeling and Simulation
- Sample- and Frame-Based Concepts (DSP System Toolbox)
Explore basic signal concepts in the context of a Simulink model. Learn more about sample-based processing and frame-based processing.
- Digital Up and Down Conversion for Family Radio Service in MATLAB (DSP System Toolbox)
Family Radio Service (FRS) is an improved walkie talkie FM radio system authorized in the United States since 1996.
- Radar Signal Simulation and Processing for Automated Driving (Radar Toolbox)
Model the hardware, signal processing, and propagation environment of an automotive radar.
Code Generation and Algorithm Acceleration
- Code Generation for a Deep Learning Simulink Model to Classify ECG Signals (Wavelet Toolbox)
Create and deploy a Simulink model for signal classification using wavelet-based features.
- Keyword Spotting in Noise Code Generation with Intel MKL-DNN (Audio Toolbox)
Generate code to spot keywords using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction.
- Deploy Signal Segmentation Deep Network on Raspberry Pi (Signal Processing Toolbox)
Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi™.