Audio Processing Using Deep Learning
|Audio Labeler||Define and visualize ground-truth labels|
Data Management and Augmentation
|Classify sounds in audio signal|
|CREPE neural network|
|Preprocess audio for CREPE deep learning network|
|Postprocess output of CREPE deep learning network|
|OpenL3 neural network|
|Extract OpenL3 features|
|Preprocess audio for OpenL3 feature extraction|
|Estimate pitch with deep learning neural network|
|VGGish neural network|
|Extract VGGish features|
|Preprocess audio for VGGish feature extraction|
|YAMNet neural network|
|Graph of YAMNet AudioSet ontology|
|Preprocess audio for YAMNet classification|
Introduction to Deep Learning for Audio Applications (Audio Toolbox)
Learn common tools and workflows to apply deep learning to audio applications.
Classify Sound Using Deep Learning (Audio Toolbox)
Train, validate, and test a simple long short-term memory (LSTM) to classify sounds.
Use transfer learning to retrain YAMNet, a pretrained convolutional neural network (CNN), to classify a new set of audio signals.
Perform speech recognition using a custom deep learning layer that implements a mel-scale filter bank.
Train a deep learning model that removes reverberation from speech.
Detect the presence of speech commands in audio using a Simulink® model.
This example shows how to classify spoken digits using both machine and deep learning techniques.
This example shows how to isolate a speech signal using a deep learning network.
This example shows a typical workflow for feature selection applied to the task of spoken digit recognition.
Use a convolutional deep network to learn a pre-emphasis filter for speech recognition.