Design and analyze speech, acoustic, and audio processing systems
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.
Audio Streaming with Sound Cards
Connect to standard laptop and desktop sound cards for streaming low-latency multichannel audio between any combination of files and live inputs and outputs.
Connectivity to Standard Audio Drivers
Read and write audio samples from and to sounds cards (such as USB or Thunderbolt™) using standard audio drivers (such as ASIO, WASAPI, CoreAudio, and ALSA) across Windows®, Mac®, and Linux® operating systems.
Low-Latency Multichannel Audio Streaming
Process live audio in MATLAB with milliseconds of round-trip latency.
Machine Learning and Deep Learning
Label, augment, create, and ingest audio and speech datasets, extract features, and compute time-frequency transformations. Develop audio and speech analytics with Statistics and Machine Learning Toolbox™, Deep Learning Toolbox™, or other machine learning tools.
Audio and Speech Feature Extraction
Extract low-level features for speech and audio analytics, including Mel frequency cepstral coefficients (MFCC), gammatone cepstral coefficients (GTCC), pitch, harmonicity, and spectral descriptors. Feed deep learning architectures working on time-series, such as those based on LSTM layers.
Transform signals into time-frequency representations using a modified discrete cosine transform (MDCT), short-time Fourier transform (STFT), or the more compact Mel-spaced spectrogram. Decompose signals by using perceptually-spaced frequency bands that use gammatone filter banks. Feed deep learning models working on two-dimensional data, such as those based on CNN layers.
Label and Create Audio Datasets
Create new recordings and assign ground-truth labels to audio and speech datasets. Automate speech transcription using speech-to-text cloud-based services.
Ingest Large Audio Datasets
Index and read from large collections of audio recordings using
audioDatastore. Randomly split lists of audio files according to labels. Parallelize processing tasks using tall arrays for data augmentation, time-frequency transformations, and feature extraction.
Audio Processing Algorithms and Effects
Generate standard waveforms, apply common audio effects, and design audio processing systems with dynamic parameter tuning and live visualization.
Audio Filters and Equalizers
Model and apply parametric EQ, graphic EQ, shelving, and variable-slope filters. Design and simulate digital crossover, octave, and fractional-octave filters.
Dynamic Range Control and Effects
Model and apply dynamic range processing algorithms such as compressor, limiter, expander, and noise gate. Add artificial reverberation with recursive parametric models.
System Simulation with Block Diagrams
Design and simulate system models using libraries of audio processing blocks for Simulink®. Tune parameters and visualize system behavior using interactive controls and dynamic plots.
Real-Time Audio Prototyping
Validate audio processing algorithms with interactive real-time listening tests in MATLAB.
Live Parameter Tuning via User Interfaces
Automatically create user interfaces for tunable parameters of audio processing algorithms. Test individual algorithms with the Audio Test Bench app and tune parameters in running programs with auto-generated interactive controls.
MIDI Connectivity for Parameter Control and Message Exchange
Interactively change parameters of MATLAB algorithms by using MIDI control surfaces. Control external hardware or respond to events by sending and receiving any type of MIDI message.
Acoustic Measurements and Spatial Audio
Measure system responses, analyze and meter signals, and design spatial audio processing systems.
Standard-Based Metering and Analysis
Apply sound pressure level (SPL) meters and loudness meters to recorded or live signals. Analyze signals with octave and fractional-octave filters. Apply standard-compliant A-, C-, or K-weighting filters to raw recordings.
Impulse Response Measurement
Measure impulse and frequency responses of acoustic and audio systems with maximum-length sequences (MLS) and exponential swept sinusoids (ESS). Get started with the Impulse Response Measurer app. Automate measurements by programmatically generating excitation signals and estimating system responses.
Efficient Convolution with Room Impulse Responses
Convolve signals with long impulse responses efficiently using frequency domain overlap-and-add or overlap-and-save implementations. Trade off latency for computational speed using automatic impulse response partitioning.
Encode and decode different ambisonic formats. Interpolate spatially sampled head-related transfer functions (HRTF).
Generate and Host Audio Plugins
Prototype audio processing algorithms written in MATLAB as standard audio plugins; use external audio plugins as regular MATLAB objects.
Generation of Audio Plugins
Generate VST and other types of audio plugins directly from MATLAB code without requiring manual design of user interfaces. With MATLAB Coder™, generate ready-to-build JUCE C++ projects for more advanced plugin prototyping.
Hosting of External Audio Plugins
Use external VST and AU plugins as regular MATLAB objects. Change plugin parameters and programmatically process MATLAB arrays. Alternatively, automate associations of plugin parameters with user interfaces and MIDI controls. Host plugins generated from your MATLAB code for increased execution efficiency.
Target Embedded and Real-Time Audio Systems
Use add-on C-code generation products to implement audio processing designs on software devices and automate connectivity to multichannel audio interfaces.
Low-Cost and Mobile Devices
Prototype audio processing designs on Raspberry Pi™ by using on-board or external multichannel audio interfaces. Create interactive control panels as mobile apps for Android® or iOS devices.
Prototype audio processing designs with single-sample inputs and outputs for adaptive noise control, hearing aid validation, or other applications requiring minimum round-trip DSP latency. Automatically target Speedgoat audio machines and ST Discovery boards directly from Simulink models.
Compute gammatone cepstral coefficients (GTCC), harmonicity, and eleven spectral descriptors for machine learning and deep learning applications
Transform signals into perceptually-spaced compact time-frequency representations
Gammatone and Octave Filter Banks
Decompose audio signals into perceptually- or logarithmically-spaced frequency bands
JUCE Plugin Project Generation
Generate a JUCE C++ project from your MATLAB audio plugin (Requires MATLAB Coder)
Plugin Parameter Tuner
Graphically tune parameters of MATLAB algorithms while running them programmatically