Baby-Cry-Gender-Recognition

Gender Recognition of babies cry using Machine Learning Algorithms(Support Vector Machine and K-Nearest Neighbors) and Signal Processing(Fast Fourier Transform and Discrete Harley Transform)

About

Machine Learning Algorithms used for Prediction:

• Support Vector Machine
• K-Nearest Neighbor

Signal Pre-processing used:

• Fast Fourier Transform
• Discrete Hartley Transform (only uses the real values of Fast Fourier Transform)

Length of wavefile used in training and testing:

• 13000 data (you can use PCA for lessening the data)

Installation

1. Use virtualenv in installing libraries and dependencies.
2. Install dependencies and libraries via pip.

To install using pip:

$ pip3 install requirements.txt

Dataset:

-datasets are on the Dataset folder. 16 sample cries for boy and girl are used (More dataset, more accurate)

Training:

1. Training consists of boy and girl dataset.
2. Either use Support Vector Machine or K-Nearest Neighbor for the Machine Learning Algorithm.
3. Either use Fast Fourier Transform or Discrete Harley Transform for the Signal Processing.

Usage:

Cut the wave files

$ python3 cutter.py

-cuts the wave files into desired length to be used as dataset or testing sample. Note: wave files must be in the same length.

Predict

$ python3 predict.py

-predict the testing sample wave file. Just input the location path of the wave file to be predicted

Sample Output:

• Frequency Sampling
• Channels
• Complete Sampling Length
• Length in seconds of the wave file
• Time response of the program