This is an implementation in Pytorch of deep learning models for automatic gender classification for the paper: A Comparison of Deep Learning Architectures for AutomaticGender Recognition from Audio Signals.
There are three different models: Fully Connected, Unidimensional Convolutional Neural Network (1D CNN) and Bidimensional Convolutional Neural Network (2D CNN).
The Librispeech corpus is used.
Run the following bash command to download and prepare the data:
./download_clean_datasets.sh
Or alternatively download the dataset that you prefer, convert the wav files to 16kHz PCM 16 bits and set the 'train', 'clean' and 'dev' directories.
To train the Fully Connected model, first, is necessary to extract the features.
To extract and save the audio features, run the following script:
python FNN_features_extraction.py -t
To train the Convolutional 2D model is necessary to create mel spectogram images. Just run the following script:
python CNN_2D.py.py --construct_images
Then, to train the models, you can just run:
- For the Fully Connected model:
python FNN.py -train
- For the 1D Convolutional:
python CNN_1D.py -train
- For the 2D Convolutional:
python CNN_2D.py -train
The relevant information related with the training configuration can be found and changed in the config.py file, inside the utils folder.
If you use this code for your research, please consider citing:
@inproceedings{eniac,
author = {Alef Ferreira and Frederico Oliveira and Nádia Silva and Anderson Soares},
title = {A Comparison of Deep Learning Architectures for Automatic Gender Recognition from Audio Signals},
booktitle = {Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional},
location = {Evento Online},
year = {2021},
keywords = {},
issn = {0000-0000},
pages = {715--726},
publisher = {SBC},
address = {Porto Alegre, RS, Brasil},
url = {https://sol.sbc.org.br/index.php/eniac/article/view/18297}
}
e-mail: [email protected]