Audio Processing Using Deep Learning

Extend deep learning workflows with audio and speech processing applications

Apply deep learning to audio and speech processing applications by using Deep Learning Toolbox™ together with Audio Toolbox™.

Apps

Audio LabelerDefine and visualize ground-truth labels

Functions

audioDatastoreDatastore for collection of audio files
audioDataAugmenterAugment audio data
audioFeatureExtractorStreamline audio feature extraction

Topics

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.

Featured Examples

Speech Command Recognition Using Deep Learning

Speech Command Recognition Using Deep Learning

Train a deep learning model that detects the presence of speech commands in audio. The example uses the Speech Commands Dataset [1] to train a convolutional neural network to recognize a given set of commands.

Open Script
Cocktail Party Source Separation Using Deep Learning Networks

Cocktail Party Source Separation Using Deep Learning Networks

Isolate a speech signal using a deep learning network.

Open Live Script
Keyword Spotting in Noise Using MFCC and LSTM Networks

Keyword Spotting in Noise Using MFCC and LSTM Networks

Identify a keyword in noisy speech using a deep learning network. In particular, the example uses a Bidirectional Long Short-Term Memory (BiLSTM) network and mel-frequency cepstral coefficients (MFCC).

Open Live Script
Denoise Speech Using Deep Learning Networks

Denoise Speech Using Deep Learning Networks

Denoise speech signals using deep learning networks. The example compares two types of networks applied to the same task: fully connected, and convolutional.

Open Live Script
Train Generative Adversarial Network (GAN) for Sound Synthesis

Train Generative Adversarial Network (GAN) for Sound Synthesis

Train and use a generative adversarial network (GAN) to generate sounds.

Open Script
Voice Activity Detection in Noise Using Deep Learning

Voice Activity Detection in Noise Using Deep Learning

Detect regions of speech in a low signal-to-noise environment using deep learning. The example uses the Speech Commands Dataset to train a Bidirectional Long Short-Term Memory (BiLSTM) network to detect voice activity.

Open Live Script
Classify Gender Using LSTM Networks

Classify Gender Using LSTM Networks

Classify the gender of a speaker using deep learning. The example uses a Bidirectional Long Short-Term Memory (BiLSTM) network and Gammatone Cepstral Coefficients (gtcc), pitch, harmonic ratio, and several spectral shape descriptors.

Open Live Script
Spoken Digit Recognition with Wavelet Scattering and Deep Learning

Spoken Digit Recognition with Wavelet Scattering and Deep Learning

Classify spoken digits using both machine and deep learning techniques. In the example, you perform classification using wavelet time scattering with a support vector machine (SVM) and with a long short-term memory (LSTM) network. You also apply Bayesian optimization to determine suitable hyperparameters to improve the accuracy of the LSTM network. In addition, the example illustrates an approach using a deep convolutional neural network (CNN) and mel-frequency spectrograms.

Open Live Script
Sequential Feature Selection for Audio Features

Sequential Feature Selection for Audio Features

A typical workflow for feature selection applied to the task of spoken digit recognition.

Open Live Script
Speech Emotion Recognition

Speech Emotion Recognition

Illustrates a simple speech emotion recognition (SER) system using a BiLSTM network. You begin by downloading the data set and then testing the trained network on individual files. The network was trained on a small German-language database [1].

Open Live Script
Acoustic Scene Recognition Using Late Fusion

Acoustic Scene Recognition Using Late Fusion

Create a multi-model late fusion system for acoustic scene recognition. The example trains a convolutional neural network (CNN) using mel spectrograms and an ensemble classifier using wavelet scattering. The example uses the TUT dataset for training and evaluation [1].

Open Script
Deep Learning Toolbox Documentation
Support