disvoice-prosody 0.0.5
A pip installable version of the prosody function from  jcvazquezc's DisVoice library

## Prosody features

```sh
prosody.py
```

Compute prosody features from continuous speech based on duration, fundamental frequency and energy.

Static or dynamic features can be computed:

Static matrix is formed with 103 features and include

Num     Feature                                                          Description
--------------------------------------------------------------------------------------------------------------------------
                                Features based on F0
---------------------------------------------------------------------------------------------------------------------------
1-6     F0-contour                                                       Avg., Std., Max., Min., Skewness, Kurtosis

7-12    Tilt of a linear estimation of F0 for each voiced segment        Avg., Std., Max., Min., Skewness, Kurtosis

13-18   MSE of a linear estimation of F0 for each voiced segment         Avg., Std., Max., Min., Skewness, Kurtosis

19-24   F0 on the first voiced segment                                   Avg., Std., Max., Min., Skewness, Kurtosis

25-30   F0 on the last voiced segment                                    Avg., Std., Max., Min., Skewness, Kurtosis

--------------------------------------------------------------------------------------------------------------------------
                                Features based on energy
---------------------------------------------------------------------------------------------------------------------------
31-34   energy-contour for voiced segments                               Avg., Std., Skewness, Kurtosis

35-38   Tilt of a linear estimation of energy contour for V segments     Avg., Std., Skewness, Kurtosis

39-42   MSE of a linear estimation of energy contour for V segment       Avg., Std., Skewness, Kurtosis

43-48   energy on the first voiced segment                               Avg., Std., Max., Min., Skewness, Kurtosis

49-54   energy on the last voiced segment                                Avg., Std., Max., Min., Skewness, Kurtosis

55-58   energy-contour for unvoiced segments                             Avg., Std., Skewness, Kurtosis

59-62   Tilt of a linear estimation of energy contour for U segments     Avg., Std., Skewness, Kurtosis

63-66   MSE of a linear estimation of energy contour for U segments      Avg., Std., Skewness, Kurtosis

67-72   energy on the first unvoiced segment                             Avg., Std., Max., Min., Skewness, Kurtosis

73-78   energy on the last unvoiced segment                              Avg., Std., Max., Min., Skewness, Kurtosis

--------------------------------------------------------------------------------------------------------------------------
                                Features based on duration
---------------------------------------------------------------------------------------------------------------------------
79      Voiced rate                                                      Number of voiced segments per second

80-85   Duration of Voiced                                               Avg., Std., Max., Min., Skewness, Kurtosis

86-91   Duration of Unvoiced                                             Avg., Std., Max., Min., Skewness, Kurtosis

92-97   Duration of Pauses                                               Avg., Std., Max., Min., Skewness, Kurtosis

98-103  Duration ratios                                                  Pause/(Voiced+Unvoiced), Pause/Unvoiced, Unvoiced/(Voiced+Unvoiced),
                                                                         Voiced/(Voiced+Unvoiced), Voiced/Puase, Unvoiced/Pause

---------------------------------------------------------------------------------------------------------------------------


The dynamic feature matrix is formed with 13 features computed for each voiced segment and contains:

- 1 Duration of the voiced segment
- 2-7. Coefficients of 5-degree Lagrange polynomial to model F0 contour
- 8-13. Coefficients of 5-degree Lagrange polynomial to model energy contour

Dynamic prosody features are based on
Najim Dehak, "Modeling Prosodic Features With Joint Factor Analysis for Speaker Verification", 2007

### Notes:

1. The fundamental frequency is computed the PRAAT algorithm. To use the RAPT method,  change the "self.pitch method" variable in the class constructor.

2. When Kaldi output is set to "true" two files will be generated, the ".ark" with the data in binary format and the ".scp" Kaldi script file

### Runing
Script is called as follows
```sh
python prosody.py <file_or_folder_audio> <file_features> <static (true or false)> <plots (true or false)> <format (csv, txt, npy, kaldi, torch)>
```


### Examples:
Extract features in the command line
```sh

python prosody.py "../audios/001_ddk1_PCGITA.wav" "prosodyfeaturesAst.txt" "true" "true" "txt"
python prosody.py "../audios/001_ddk1_PCGITA.wav" "prosodyfeaturesUst.csv" "true" "true" "csv"
python prosody.py "../audios/001_ddk1_PCGITA.wav" "prosodyfeaturesUdyn.pt" "false" "true" "torch"

python prosody.py "../audios/" "prosodyfeaturesst.txt" "true" "false" "txt"
python prosody.py "../audios/" "prosodyfeaturesst.csv" "true" "false" "csv"
python prosody.py "../audios/" "prosodyfeaturesdyn.pt" "false" "false" "torch"
python prosody.py "../audios/" "prosodyfeaturesdyn.csv" "false" "false" "csv"

KALDI_ROOT=/home/camilo/Camilo/codes/kaldi-master2
export PATH=$PATH:$KALDI_ROOT/src/featbin/
python prosody.py "../audios/001_ddk1_PCGITA.wav" "prosodyfeaturesUdyn" "false" "false" "kaldi"

python prosody.py "../audios/" "prosodyfeaturesdyn" "false" "false" "kaldi"
```

Extract features directly in Python
```
from prosody import Prosody
prosody=Prosody()
file_audio="../audios/001_ddk1_PCGITA.wav"
features1=prosody.extract_features_file(file_audio, static=True, plots=True, fmt="npy")
features2=prosody.extract_features_file(file_audio, static=True, plots=True, fmt="dataframe")
features3=prosody.extract_features_file(file_audio, static=False, plots=True, fmt="torch")
prosody.extract_features_file(file_audio, static=False, plots=False, fmt="kaldi", kaldi_file="./test")
```

[Jupyter notebook](https://github.com/jcvasquezc/DisVoice/blob/master/notebooks_examples/prosody_features.ipynb)

#### Results:

Prosody analysis from continuous speech static
![Image](https://raw.githubusercontent.com/jcvasquezc/DisVoice/master/images/prosody1.png)

![Image](https://raw.githubusercontent.com/jcvasquezc/DisVoice/master/images/prosody3.png)


#### References

[[1]](http://ieeexplore.ieee.org/abstract/document/4291597/). N., Dehak, P. Dumouchel, and P. Kenny. "Modeling prosodic features with joint factor analysis for speaker verification." IEEE Transactions on Audio, Speech, and Language Processing 15.7 (2007): 2095-2103.

[[2]](http://www.sciencedirect.com/science/article/pii/S105120041730146X). J. R. Orozco-Arroyave, J. C. Vásquez-Correa et al. "NeuroSpeech: An open-source software for Parkinson's speech analysis." Digital Signal Processing (2017).