Audio Processing Using Deep Learning
|Signal Labeler||Label signal attributes, regions, and points of interest, and extract features|
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 feature embeddings|
|Preprocess audio for OpenL3 feature extraction|
|Estimate pitch with deep learning neural network|
|VGGish neural network|
|Extract VGGish feature embeddings|
|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.
- Transfer Learning with Pretrained Audio Networks in Deep Network Designer
This example shows how to interactively fine-tune a pretrained network to classify new audio signals using Deep Network Designer.
- Speaker Identification Using Custom SincNet Layer and Deep Learning
Perform speech recognition using a custom deep learning layer that implements a mel-scale filter bank.
- Dereverberate Speech Using Deep Learning Networks
Train a deep learning model that removes reverberation from speech.
- Speech Command Recognition in Simulink
Detect the presence of speech commands in audio using a Simulink® model.
- Sequential Feature Selection for Audio Features
This example shows a typical workflow for feature selection applied to the task of spoken digit recognition.
- Train Spoken Digit Recognition Network Using Out-of-Memory Audio Data
This example trains a spoken digit recognition network on out-of-memory audio data using a transformed datastore.
- Train Spoken Digit Recognition Network Using Out-of-Memory Features
This example trains a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore.
- Investigate Audio Classifications Using Deep Learning Interpretability Techniques
This example shows how to use interpretability techniques to investigate the predictions of a deep neural network trained to classify audio data.