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Implements the Linear and Mel Frequency Cepstal Coefficients (MFCC and LFCC)

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.

Endpoint Name | Data Format | Nature |
---|---|---|

speech | system/array_1d_floats/1 | Input |

vad | system/array_1d_integers/1 | Input |

features | system/array_2d_floats/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 MFCC and LFCC feature extraction. It relies on the Bob library.

The following parameters are set inside the algorithm and can be modified by the user:

- 'win_length_ms': length of the processing window
- 'win_shift_ms': length of the shift
- 'n_filters': number of filters
- 'n_ceps': number of cepstal coefficients
- 'f_min': minimum frequency
- 'f_max': maximum frequency
- 'delta_win': window on which first and second derivatives are computed
- 'pre_emphasis_coeff': pre-emphasis coefficient
- 'mel_scale': flag for Mel scale
- 'dct_norm': DCT normalization
- 'with_delta': flag for computing the first derivatives
- 'with_delta_delta': flag for computing the second derivatives
- 'with_energy': flag for computing the energy
- 'features_mask': mask to use only a sub-set of features
- 'normalizeFeatures': flag to do zero-mean and variance normalization

No experiments are using this algorithm.

This algorithm was never executed.

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