This project is to test performance of different classification method on Gender Detection problem
example:
python GenderDetect.py [options] wave_file model_file where options may include: -m Model --determine which model you want to use, including GMM,MLP,KNN,DBN(currently, only GMM is available) -l window length --the length of the analysis window in seconds. Default is 0.025s (25 milliseconds) -s window step -- the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
Explanation for the process of training.
| Document | Description |
|---|---|
| voxforgeDownload.sh | Download voxforge corpus from website |
| Corpus_prep.ipynb | Pick and formalize the audios for further training |
| MFCC_extract.ipynb | Extract AMFCC features from prepared corpus |
| GMM_classify.ipynb | Train GMM models for gender detection |
| base.py | Provides mfcc features for use in ASR applications |
| sigproc.py | routines for basic signal processing |
| GMM.pk | Trained GMM models |
| male_example.wav | a male audio |
| female_example.wav | a female audio |