Reconstruct electrocardiogram signals using a bidirectional long short-term memory network and wavelet multiresolution analysis.

Segment human electrocardiogram signals using time-frequency analysis and deep learning.

Classify heartbeat electrocardiogram data using deep learning and signal processing.

Classify heartbeat electrocardiogram data using deep learning and the continuous wavelet transform.

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 learning and deep learning techniques.

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 to classify electrocardiogram signals using wavelet-derived features.

Classify human electrocardiogram signals on a Raspberry Pi using scalograms and a deep convolutional neural network.