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

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.

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

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

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

Use an adversarial learning denoiser model to denoise signals.

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

Classify parasitic infections using wavelet image scattering and deep learning.

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

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.

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

Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.

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].

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

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).

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).