# AI for Signals

Signal Processing Toolbox™ provides functionality to perform signal labeling, feature engineering, and dataset generation for machine learning and deep learning workflows. The toolbox also offers an autoencoder object that you can train and use to detect anomalies in signal data.

## Apps

Signal Analyzer | Visualize and compare multiple signals and spectra |

Signal Labeler | Label signal attributes, regions, and points of interest, and extract features |

EDF File Analyzer | View EDF or EDF+ files (Since R2021a) |

Experiment Manager | Design and run experiments to train and compare deep
learning networks (Since R2020a) |

## Functions

## Topics

**Manage Data Sets for Machine Learning and Deep Learning Workflows**Organize, access, and manage data sets for different AI applications.

**Choose an App to Label Ground Truth Data**Decide which app to use to label ground truth data: Image Labeler, Video Labeler, Ground Truth Labeler, Lidar Labeler, Signal Labeler, or Medical Image Labeler.

**Radar and Communications Waveform Classification Using Deep Learning (Phased Array System 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.

**Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)**Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.

**Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)**Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.

**Anomaly Detection Using Autoencoder and Wavelets**Use wavelet-extracted features and an autoencoder to detect arc signals in a DC system.

**Detect Anomalies in ECG Data Using Wavelet Scattering and LSTM Autoencoder in Simulink**Use wavelet scattering and a deep learning network within a Simulink

^{®}model to detect anomalies in ECG signals.**Train Spoken Digit Recognition Network Using Out-of-Memory Features**Train a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore.

**Denoise Speech Using Deep Learning Networks**Denoise speech signals using fully connected and convolutional neural networks.

**Classify Time Series Using Wavelet Analysis and Deep Learning**Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network.

**Spectral Descriptors (Audio Toolbox)**Overview and applications of spectral descriptors.

## Related Information

**Deep Learning in MATLAB (Deep Learning Toolbox)****Sequence Classification Using Deep Learning (Deep Learning Toolbox)****How to Set Up and Manage Experiments in MATLAB**