Implements the energy-based voice activity detection

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Unnamed group

Endpoint Name Data Format Nature
speech system/array_1d_floats/1 Input
vad system/array_1d_integers/1 Output

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
rate float64 16000.0

The code for this algorithm in Python
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This algorithm implements the energy-based voice activity detection. It models the energy into two Gaussian distributions. It assumes that the Gaussian with the highest mean corresponds to speech whereas the the Gaussian with the lowest mean corresponds to non-speech (typically silence).

The following parameters are set inside the script:

  • win_length_ms: length of the processing window
  • win_shift_ms: length of the shift
  • max_iterations: maximum iterations of the k-means training
  • smoothing_window: smoothing window for speech detection
No experiments are using this algorithm.

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

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