get started with audio toolbox -凯发k8网页登录

audio toolbox™ provides tools for audio processing, speech analysis, and acoustic measurement. it includes algorithms for processing audio signals such as equalization and time stretching, estimating acoustic signal metrics such as loudness and sharpness, and extracting audio features such as mfcc and pitch. it also provides advanced machine learning models, including i-vectors, and pretrained deep learning networks, including vggish and crepe. toolbox apps support live algorithm testing, impulse response measurement, and signal labeling. the toolbox provides streaming interfaces to asio™, coreaudio, and other sound cards; midi devices; and tools for generating and hosting vst and audio units plugins.

with audio toolbox you can import, label, and augment audio data sets, as well as extract features to train machine learning and deep learning models. the pre-trained models provided can be applied to audio recordings for high-level semantic analysis.

you can prototype audio processing algorithms in real time or run custom acoustic measurements by streaming low-latency audio to and from sound cards. you can 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 as regular matlab^® objects.

tutorials

• 
  

  read audio from a file and write audio to speakers.

• 
  

  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.

• 
  

  train, validate, and test a simple long short-term memory (lstm) to classify sounds.

• 
  

  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.

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

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.

videos

design and test audio processing systems with audio toolbox.

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.