Get Started with Audio Toolbox

Audio Toolbox™ provides tools for audio processing, speech analysis, and acoustic measurement. It includes algorithms for audio signal processing (such as equalization and dynamic range control) and acoustic measurement (such as impulse response estimation, octave filtering, and perceptual weighting). It also provides algorithms for audio and speech feature extraction (such as MFCC and pitch) and audio signal transformation (such as gammatone filter bank and Mel-spaced spectrogram).

Toolbox apps support live algorithm testing, impulse response measurement, and audio signal labeling. The toolbox provides streaming interfaces to ASIO™, WASAPI, ALSA, and CoreAudio sound cards and MIDI devices, and tools for generating and hosting standard audio plugins such as VST and Audio Units.

With Audio Toolbox you can import, label, and augment audio data sets, as well as extract features and transform signals for machine learning and deep learning. You can prototype audio processing algorithms in real time by streaming low-latency audio while tuning parameters and visualizing signals. You can also validate your algorithm by turning it into an audio plugin to run in external host applications such as Digital Audio Workstations. Plugin hosting lets you use external audio plugins like regular objects to process MATLAB^® arrays. Sound card connectivity enables you to run custom measurements on real-world audio signals and acoustic systems.

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

• Introduction to 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 Example

Videos

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

Introduction to Deep Learning for Audio and Speech Applications
Create or ingest datasets, extract features, and develop audio and speech analytics using Statistics and Machine Learning Toolbox, Deep Learning Toolbox, or other machine learning tools.