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Pretrained Models

Transfer learning, sound classification, feature embeddings, pretrained audio deep learning networks

Audio Toolbox™ provides MATLAB® and Simulink® support for pretrained audio deep learning networks. Locate and classify sounds with YAMNet and estimate pitch with CREPE. Extract VGGish or OpenL3 feature embeddings to input to machine learning and deep learning systems. Use i-vector systems to produce compact representations of audio signals for applications such as speaker recognition, verification, identification, and diarization, speech emotion recognition, and acoustic machine fault detection.

This functionality requires Deep Learning Toolbox™. The Audio Toolbox pretrained networks are available in Deep Network Designer (Deep Learning Toolbox).

Functions

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vggishVGGish neural network
vggishPreprocessPreprocess audio for VGGish feature extraction
vggishEmbeddingsExtract VGGish feature embeddings
classifySoundClassify sounds in audio signal
yamnetYAMNet neural network
yamnetGraphGraph of YAMNet AudioSet ontology
yamnetPreprocessPreprocess audio for YAMNet classification
openl3OpenL3 neural network
openl3PreprocessPreprocess audio for OpenL3 feature extraction
openl3EmbeddingsExtract OpenL3 feature embeddings
crepeCREPE neural network
crepePreprocessPreprocess audio for CREPE deep learning network
crepePostprocessPostprocess output of CREPE deep learning network
pitchnnEstimate pitch with deep learning neural network
ivectorSystemCreate i-vector system
speakerRecognitionPretrained speaker recognition system

Blocks

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VGGish EmbeddingsExtract VGGish embeddings
VGGish PreprocessPreprocess audio for VGGish feature extraction
VGGishVGGish embeddings extraction network
Sound ClassifierClassify sounds in audio signal
YAMNetYAMNet sound classification network
YAMNet PreprocessPreprocess audio for YAMNet classification
OpenL3 EmbeddingsExtract OpenL3 embeddings
OpenL3 PreprocessPreprocess audio for OpenL3 embeddings extraction
OpenL3OpenL3 embeddings extraction network

Apps

Deep Network DesignerDesign, visualize, and train deep learning networks

Topics