グラウンド トゥルース データにラベルを付けるためのアプリの選択

グラウンド トゥルース データのラベル付けに、次のどのアプリを使用するかを決定します。イメージ ラベラー、ビデオ ラベラー、グラウンド トゥルース ラベラー、LIDAR ラベラー、信号ラベラー、Audio Labeler。

Radar and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)

This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Music Genre Classification Using Wavelet Time Scattering (Wavelet Toolbox)

This example shows how to classify the genre of a musical excerpt using wavelet time scattering and the audio datastore. In wavelet scattering, data is propagated through a series of wavelet transforms, nonlinearities, and averaging to produce low-variance representations of the data. These low-variance representations are then used as inputs to a classifier.

Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)

This example shows how to classify human phonocardiogram (PCG) recordings using wavelet time scattering and a support vector machine (SVM) classifier. Phonocardiograms are acoustic recordings of sounds produced by the systolic and diastolic phases of the heart. Auscultation of the heart continues to play an important diagnostic role in assessing cardiac health. Unfortunately, many areas of the world lack sufficient numbers of medical personnel trained in heart auscultation. Accordingly, it is necessary to develop reliable automated ways of interpreting phonocardiogram data.