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VGGish Embeddings

Extract VGGish embeddings

  • Library:
  • Audio Toolbox / Deep Learning

  • VGGish Embeddings block

Description

The VGGish Embeddings block uses VGGish to extract feature embeddings from audio segments. The VGGish Embeddings block combines necessary audio preprocessing and VGGish network inference and returns feature embeddings that are a compact representation of audio data.

Ports

Input

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Sound data, specified as a one-channel signal (column vector). If Sample rate of input signal (Hz) is 16e3, there are no restrictions on the input frame length. If Sample rate of input signal (Hz) is different from 16e3, then the input frame length must be a multiple of the decimation factor of the resampling operation that the block performs. If the input frame length does not satisfy this condition, the block throws an error message with information on the decimation factor.

Data Types: single | double

Output

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VGGish feature embeddings, returned as a row vector of length 128. The feature embeddings are a compact representation of audio data.

Data Types: single

Parameters

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Sample rate of the input signal in Hz, specified as a positive scalar.

Specify the overlap percentage between consecutive mel spectrograms as a scalar in the range [0 100).

Block Characteristics

Data Types

double | single

Direct Feedthrough

no

Multidimensional Signals

no

Variable-Size Signals

no

Zero-Crossing Detection

no

Algorithms

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References

[1] Gemmeke, Jort F., Daniel P. W. Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R. Channing Moore, Manoj Plakal, and Marvin Ritter. “Audio Set: An Ontology and Human-Labeled Dataset for Audio Events.” In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 776–80. New Orleans, LA: IEEE, 2017. https://doi.org/10.1109/ICASSP.2017.7952261.

[2] Hershey, Shawn, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke, Aren Jansen, R. Channing Moore, Manoj Plakal, et al. “CNN Architectures for Large-Scale Audio Classification.” In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 131–35. New Orleans, LA: IEEE, 2017. https://doi.org/10.1109/ICASSP.2017.7952132.

Extended Capabilities

Version History

Introduced in R2022a