Use stochastic ray tracing to simulate the impulse response of a simple room.

Simulate the impulse response of a "shoebox" (cuboid) empty room.

Measure engine noise and use psychoacoustic metrics to model its perceived loudness, sharpness, fluctuation strength, roughness, and overall annoyance level.

Design octave-band and fractional octave-band filters, including filter banks and octave SPL meters.

Obtain A-weighting and C-weighting filters the weightingFilter System object in the Audio Toolbox™.

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.

Design a real-time active noise control system using a Speedgoat® Simulink® Real-Time™ target.

This examples shows how to create ambisonic plugins using MATLAB® higher order ambisonic (HOA) demo functions. Ambisonics is a spatial audio technique which represents a three-dimensional sound field using spherical harmonics. This example contains an encoder plugin, a function to generate custom encoder plugins, a decoder plugin, and a function to generate custom decoder plugins. The customization of plugin generation enables a user to specify various ambisonic orders and various device lists for a given ambisonic configuration.

Decode ambisonic audio into binaural audio using virtual loudspeakers. A virtual loudspeaker is a sound source positioned on the surface of a sphere, with the listener located at the center of the sphere. Each virtual loudspeaker has a pair of Head-Related Transfer Functions (HRTF) associated with it: one for the left ear and one for the right ear. The virtual loudspeaker locations along with the ambisonic order are used to calculate the ambisonic decoder matrix. The output of the decoder is filtered by the HRTFs corresponding to the virtual loudspeaker position. The signals from the left HRTFs are summed together and fed to the left ear. The signals from the right HRTFs are summed together and fed to the right ear. A block diagram of the audio signal flow is shown here.

Use tools from Audio Toolbox to measure loudness, loudness range, and true-peak value.

Illustrates microphone array beamforming to extract desired speech signals in an interference-dominant, noisy environment. Such operations are useful to enhance speech signal quality for perception or further processing. For example, the noisy environment can be a trading room, and the microphone array can be mounted on the monitor of a trading computer. If the trading computer must accept speech commands from a trader, the beamformer operation is crucial to enhance the received speech quality and achieve the designed speech recognition accuracy.

Beamform signals received by an array of microphones to extract a desired speech signal in a noisy environment. It uses the dataflow domain in Simulink® to partition the data-driven portions of the system into multiple threads and thereby improving the performance of the simulation by executing it on your desktop's multiple cores.

Acquire and process live multichannel audio. It also presents a simple algorithm for estimating the Direction Of Arrival (DOA) of a sound source using multiple microphone pairs within a linear array.

Several basic aspects of audio signal positioning. The listener occupies a location in the center of a circle, and the position of the sound source is varied so that it remains within the circle. In this example, the sound source is a monaural recording of a helicopter. The sound field is represented by five discrete speaker locations on the circumference of the circle and a low-frequency output that is presumed to be in the center of the circle.

Track head orientation by fusing data received from an IMU, and then control the direction of arrival of a sound source by applying head-related transfer functions (HRTF).

Generate a stereo signal from a multichannel audio signal using matrix encoding, and how to recover the original channels from the stereo mix using matrix decoding. This example illustrates MATLAB® and Simulink® implementations. This example also shows how performance can be improved by using dataflow execution domain.

The impulse response (IR) is an important tool for characterizing or representing a linear time-invariant (LTI) system. The impulseResponseMeasurer enables you to measure and capture the impulse response of audio systems, including:

Measure total harmonic distortion and noise level of audio input and output devices.

Apply adaptive filters to acoustic echo cancellation (AEC).