Signal Processing
MathWorks® products provide workflows for audio, deep learning, and signal processing applications. You can combine language-based programming and Simulink® block diagrams to preprocess, visualize, and analyze time series, develop and debug algorithms, design and apply filters, and model and test systems. Use MATLAB® Coder™ and GPU Coder™ to deploy your solutions onto hardware.
Products for Signal Processing
Topics
Filtering
- 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.
Measurements
- Detect Outbreaks and Significant Changes in Signals (Signal Processing Toolbox)
Determine significant changes or outbreaks in signals using cumulative sums and changepoint detection. - 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.
Visualization
- Use Signal Analyzer App (Signal Processing Toolbox)
Visualize, measure, analyze, and compare signals in the time, frequency, and time-frequency domains. - Using Wavelet Time-Frequency Analyzer App (Wavelet Toolbox)
Learn how to use to visualize scalograms of 1-D signals and recreate results in your workspace. - 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 Time-Frequency Analysis Using the Continuous Wavelet Transform (Wavelet Toolbox)
Perform and interpret time-frequency analysis of signals using the continuous wavelet transform. - 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. (Since R2022a) - Deep Learning for Audio Applications (Audio Toolbox)
Learn common tools and workflows to apply deep learning to audio applications. - Detect Air Compressor Sounds in Simulink Using Wavelet Scattering (DSP System Toolbox)
Use the Wavelet Scattering block and a pretrained deep learning network to classify audio signals.
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.
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®. (Since R2020b)
Related Information
Featured Examples
Classify Time Series Using Wavelet Analysis and Deep Learning
Classify electrocardiogram data using deep learning and the continuous wavelet transform.
AI for Speech Command Recognition
Build, train, compress, and deploy a deep learning model for speech command recognition.
Practical Introduction to Frequency-Domain Analysis
Perform and interpret basic frequency-domain signal analysis using simulated and real data.
Signal Recovery with Differentiable Scalograms and Spectrograms
Use differentiable time-frequency transforms to recover a time-domain signal without the need for phase information or transform inversion.
Apply Adaptive Noise Canceling to Fetal Electrocardiography
Apply adaptive filters to noise removal using adaptive noise canceling (ANC) in fetal electrocardiography.
Room Impulse Response Simulation with the Image-Source Method and HRTF Interpolation
Simulate the impulse response of a "shoebox" (cuboid) empty room.
