### Glottal source features
```sh
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
```sh
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:
```sh
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](https://github.com/jcvasquezc/DisVoice/blob/master/notebooks_examples/glottal_features.ipynb)
#### Results:
Glottal analysis from a sustained vowel
!
#### References
[[1]](https://link.springer.com/chapter/10.1007/978-3-319-45510-5_46) 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.
