Signal Processing Using Deep Learning

Extend deep learning workflows with signal processing applications

Apply deep learning to signal processing by using Deep Learning Toolbox™ together with Signal Processing Toolbox™ or Wavelet Toolbox™. For audio and speech processing applications, seeAudio Processing Using Deep Learning. For applications in wireless communications, seeWireless Communications Using Deep Learning.

Apps

Signal Labeler

Label signal attributes, regions, and points of interest, and extract features

Functions

`labeledSignalSet`	Create labeled signal set
`signalLabelDefinition`	Create signal label definition
`signalMask`	Modify and convert signal masks and extract signal regions of interest
`countlabels`	Count number of unique labels
`folders2labels`	Get list of labels from folder names
`splitlabels`	Find indices to split labels according to specified proportions
`signalDatastore`	Datastore for collection of signals
`dlstft`	Deep learning short-time Fourier transform
`stftLayer`	Short-time Fourier transform layer

Topics

Pedestrian and Bicyclist Classification Using Deep Learning(Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.
Radar and Communications Waveform Classification Using Deep Learning(Radar Toolbox)
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
Label Radar Signals with Signal Labeler(Radar Toolbox)
Label the time and frequency features of pulse radar signals with added noise.
Radar Target Classification Using Machine Learning and Deep Learning(Radar Toolbox)
Classify radar returns using machine and deep learning approaches.
Automate Signal Labeling with Custom Functions(Signal Processing Toolbox)
UseSignal Labelerto locate and label QRS complexes and R peaks of ECG signals.
Crack Identification from Accelerometer Data(Wavelet Toolbox)
Use wavelet and deep learning techniques to detect transverse pavement cracks and localize their position.
Iterative Approach for Creating Labeled Signal Sets with Reduced Human Effort(Signal Processing Toolbox)
Use deep learning to decrease the human effort required to label signals.
Label Signal Attributes, Regions of Interest, and Points(Signal Processing Toolbox)
UseSignal Labelerto label attributes, regions, and points of interest in a set of whale songs.
Automate Signal Labeling with Custom Functions(Signal Processing Toolbox)
UseSignal Labelerto locate and label QRS complexes and R peaks of ECG signals.
Classify Arm Motions Using EMG Signals and Deep Learning(Signal Processing Toolbox)
Classify arm motions using labeled EMG signals and a long short-term memory network.
GPU Acceleration of Scalograms for Deep Learning(Wavelet Toolbox)
Use your GPU to accelerate feature extraction for signal classification.
Denoise EEG Signals Using Deep Learning Regression(Signal Processing Toolbox)
Remove EOG noise from EEG signals using deep learning regression.

Featured Examples

Waveform Segmentation Using Deep Learning

Segment human electrocardiogram (ECG) signals using recurrent deep learning networks and time-frequency analysis.

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Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram (ECG) data from the PhysioNet 2017 Challenge using deep learning and signal processing. In particular, the example uses Long Short-Term Memory networks and time-frequency analysis.

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Classify Time Series Using Wavelet Analysis and Deep Learning

Classify human electrocardiogram (ECG) signals using the continuous wavelet transform (CWT) and a deep convolutional neural network (CNN).

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Generate Synthetic Signals Using Conditional GAN

Use a conditional generative adversarial network to produce synthetic data for model training.

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Learn Pre-Emphasis Filter Using Deep Learning

Use a convolutional deep network to learn a pre-emphasis filter for speech recognition.

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Denoise Signals with Adversarial Learning Denoiser Model

Use an adversarial learning denoiser model to denoise signals.

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Hand Gesture Classification Using Radar Signals and Deep Learning

Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.

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Parasite Classification Using Wavelet Scattering and Deep Learning

Classify parasitic infections using wavelet image scattering and deep learning.

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Fault Detection Using Wavelet Scattering and Recurrent Deep Networks

Classify faults in acoustic recordings of air compressors using a wavelet scattering network paired with a recurrent neural network.

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Spoken Digit Recognition with Wavelet Scattering and Deep Learning

Classify spoken digits using both machine and deep learning techniques. In the example, you perform classification using wavelet time scattering with a support vector machine (SVM) and with a long short-term memory (LSTM) network. You also apply Bayesian optimization to determine suitable hyperparameters to improve the accuracy of the LSTM network. In addition, the example illustrates an approach using a deep convolutional neural network (CNN) and mel-frequency spectrograms.

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Anomaly Detection Using Autoencoder and Wavelets

Use wavelet-extracted features and an autoencoder to detect arc signals in a DC system.

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Detect Anomalies Using Wavelet Scattering with Autoencoders

学习如何开发predictiv警报系统e maintenance using wavelet scattering and deep learning.

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Acoustic Scene Recognition Using Late Fusion

Create a multi-model late fusion system for acoustic scene recognition. The example trains a convolutional neural network (CNN) using mel spectrograms and an ensemble classifier using wavelet scattering. The example uses the TUT dataset for training and evaluation [1].

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Deploy Signal Segmentation Deep Network on Raspberry Pi

Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi™.

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Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning

Generate and deploy a CUDA® executable that classifies human electrocardiogram (ECG) signals using features extracted by the continuous wavelet transform (CWT) and a pretrained convolutional neural network (CNN).

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Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi

The workflow to classify human electrocardiogram (ECG) signals using the Continuous Wavelet Transform (CWT) and a deep convolutional neural network (CNN). This example also provides information on how to generate and deploy the code and CNN for prediction on a Raspberry Pi target (ARM®-based device).

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