Get Started with Audio Toolbox

Design and analyze speech, acoustic, and audio processing systems

Audio Toolbox™ provides signal processing and analysis tools for audio, speech, and acoustics. It includes algorithms for processing audio signals, estimating acoustic metrics, labeling and augmenting audio data sets, and extracting audio features.

You can run measurements or prototype algorithms in real time by streaming low-latency audio to and from ASIO, CoreAudio, and other sound cards. The toolbox lets you control algorithm parameters via graphical interfaces or MIDI events. You can validate your algorithm by turning it into a VST or Audio Unit plugin to run in external host applications. The toolbox also offers plugin hosting, so you can process MATLAB^® arrays using external audio plugins.

The toolbox includes pre-trained machine learning and deep learning models that support transfer learning. You can apply the models directly to speech and acoustic signals for high-level tasks such as embedding extraction, sound classification, speaker verification, speech transcription and synthesis, source separation, and background noise reduction.

Acknowledgements

Installation and Configuration

Sound Cards and Drivers

Tutorials

Audio Input and Audio Output

Read audio from a file and write audio to speakers.
Process and Analyze Streaming Audio

Create an audio test bench and apply real-time processing.
Real-Time Audio in Simulink

Create a model using the Simulink^® templates and blocks for audio processing.
Classify Sound Using Deep Learning

Train, validate, and test a simple long short-term memory (LSTM) to classify sounds.
Transfer Learning with Pretrained Audio Networks

Use transfer learning to retrain YAMNet, a pretrained convolutional neural network (CNN), to classify a new set of audio signals.
Design an Audio Plugin

Create a simple audio plugin in MATLAB and then use it to generate a VST plugin.

About Audio Plugins

What Are DAWs, Audio Plugins, and MIDI Controllers?

Learn about the role of digital audio workstations (DAWs), audio plugins, and Musical Instrument Digital Interface (MIDI) controllers in designing audio processing algorithms.

About Deep Learning and Machine Learning for Audio

Deep Learning for Audio Applications

Learn common tools and workflows to apply deep learning to audio applications.

Featured Examples

Measure Audio Latency

Measure the latency of an audio device. The example uses audioLatencyMeasurementExampleApp which in turn uses audioPlayerRecorder along with a test signal and cross correlation to determine latency. To avoid disk access interference, the test signal is loaded into a dsp.AsyncBuffer object first, and frames are streamed from that object through the audio device.

Open Script

Delay-Based Audio Effects

Design and use three audio effects that are based on varying delay: echo, chorus and flanger. The example also shows how the algorithms, developed in MATLAB, can be easily ported to Simulink.

Open Live Script

Sound Pressure Measurement of Octave Frequency Bands

Demonstrates how to measure sound pressure levels of octave frequency bands. A user interface (UI) enables you to experiment with various parameters while the measurement is displayed.

Open Script

Videos

What Is Audio Toolbox?
Design and test audio processing systems with Audio Toolbox.

Introduction to MATLAB for Deep Learning in Audio and Speech Applications
Learn how to use deep learning in classification and detection applications with audio, speech, and acoustic data in this introduction to some of the fundamental capabilities in MATLAB, Audio Toolbox, and Deep Learning Toolbox™.