Classify heartbeat electrocardiogram data using deep learning and signal processing.

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

Use deep learning to decrease the human effort required to label signals.

Use a conditional generative adversarial network to produce synthetic signals.

Classify spoken digits using a deep convolutional neural network and a custom spectrogram layer.

Use Signal Labeler to label attributes, regions, and points of interest in a set of whale songs.

Use Signal Labeler to locate and label QRS complexes and R peaks of ECG signals.

Use Signal Labeler to label spoken words in an audio signal.

Denoise speech signals using fully connected and convolutional neural networks.

Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network.

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

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

Remove EOG noise from EEG signals using deep learning regression.

Use an adversarial learning denoiser model to denoise signals.

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

Extract features from smartphone sensor signals and use them to classify human activity.

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

Classify arm motions using labeled EMG signals and a long short-term memory network.