Get Started with Audio Toolbox
R2026bAudio Toolbox™ provides signal processing, analysis, and simulation tools for audio, speech, and acoustics. It includes audio processing algorithms, psychoacoustic metric estimation, acoustic propagation modeling, data labeling and augmentation, and feature extraction. The toolbox also includes pretrained machine learning and deep learning models for transfer learning and tasks such as classification, transcription, synthesis, and source separation.
You can stream low-latency audio to prototype algorithms and measure signals in real time. The toolbox enables you to simulate audio and acoustic systems by combining physical acoustics, audio DSP, and psychoacoustic evaluation to assess perceptual effects earlier in the design process. You can validate designs by generating VST or Audio Unit plugins, and process audio samples using AI models and hosted audio plugins.
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 Signal Processing
- Sample- and Frame-Based Concepts
Explore basic signal concepts in the context of a Simulink model. Learn more about sample-based processing and frame-based processing.
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.
About Technical Standards for Audio
- Technical Standards Implemented in Audio Toolbox
Technical standards and common industry metrics implemented in Audio Toolbox.
Featured Examples
Measure Audio Latency
Measure the latency of an audio device. This example uses audiostreamer to measure latency by cross-correlation, by playing and recording a brief sine sweep.
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®.
Sound Pressure Measurement of Octave Frequency Bands
Measure sound pressure levels of octave frequency bands using splMeter. An interactive app lets you adjust the frequency weighting, bandwidth, and audio source while viewing live measurements.
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™.
