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A pip installable version of the glottal function from jcvazquezc's DisVoice library

Project description

Glottal source features

glottal.py

Compute phonation features derived from the glottal source reconstruction from sustained vowels.

Nine descriptors are computed:

  1. Variability of time between consecutive glottal closure instants (GCI)
  2. Average opening quotient (OQ) for consecutive glottal cycles-> rate of opening phase duration / duration of glottal cycle
  3. Variability of opening quotient (OQ) for consecutive glottal cycles-> rate of opening phase duration /duration of glottal cycle
  4. Average normalized amplitude quotient (NAQ) for consecutive glottal cycles-> ratio of the amplitude quotient and the duration of the glottal cycle
  5. Variability of normalized amplitude quotient (NAQ) for consecutive glottal cycles-> ratio of the amplitude quotient and the duration of the glottal cycle
  6. Average H1H2: Difference between the first two harmonics of the glottal flow signal
  7. Variability H1H2: Difference between the first two harmonics of the glottal flow signal
  8. Average of Harmonic richness factor (HRF): ratio of the sum of the harmonics amplitude and the amplitude of the fundamental frequency
  9. Variability of HRF

Static or dynamic matrices can be computed:

Static matrix is formed with 36 features formed with (9 descriptors) x (4 functionals: mean, std, skewness, kurtosis)

Dynamic matrix is formed with the 9 descriptors computed for frames of 200 ms length.

Notes:

  1. The fundamental frequency is computed using the RAPT algorithm.

Script is called as follows

python glottal.py <file_or_folder_audio> <file_features.txt> [dynamic_or_static (default static)] [plots (true or false) (default false)] [kaldi output (true or false) (default false)]

Examples:

python glottal.py "../audios/001_a1_PCGITA.wav" "glottalfeaturesAst.txt" "true" "true" "txt"
python glottal.py "../audios/098_u1_PCGITA.wav" "glottalfeaturesUst.csv" "true" "true" "csv"
python glottal.py "../audios/098_u1_PCGITA.wav" "glottalfeaturesUdyn.pt" "false" "true" "torch"

python glottal.py "../audios/" "glottalfeaturesst.txt" "true" "false" "txt"
python glottal.py "../audios/" "glottalfeaturesst.csv" "true" "false" "csv"
python glottal.py "../audios/" "glottalfeaturesdyn.pt" "false" "false" "torch"

KALDI_ROOT=/home/camilo/Camilo/codes/kaldi-master2
export PATH=$PATH:$KALDI_ROOT/src/featbin/
python glottal.py "../audios/098_u1_PCGITA.wav" "glottalfeaturesUdyn" "false" "false" "kaldi"

python glottal.py "../audios/" "glottalfeaturesdyn" "false" "false" "kaldi"

Jupyter notebook

Results:

Glottal analysis from a sustained vowel !Image

References

[1] Belalcázar-Bolaños, E. A., Orozco-Arroyave, J. R., Vargas-Bonilla, J. F., Haderlein, T., & Nöth, E. (2016, September). Glottal Flow Patterns Analyses for Parkinson’s Disease Detection: Acoustic and Nonlinear Approaches. In International Conference on Text, Speech, and Dialogue (pp. 400-407). Springer.

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