Gender Recognition of Voice Samples

Machine Learning/ Classification Project/Python and R

Objectives

To predict gender with corresponding voice and speech features

Authors: Amanraj Singh, Monica Sharma

Download the Data

The dataset can be downloaded here on Kaggle. It is consisted of 3,168 observations with the 21 variables, as listed below.

1 target variable:
label (male or female)
20 independent variables:


meanfreq: mean frequency (in kHz)
sd: standard deviation of frequency
median: median frequency (in kHz)
Q25: first quantile (in kHz)
Q75: third quantile (in kHz)
IQR: interquantile range (in kHz)
skew: skewness (see note in specprop description)
kurt: kurtosis (see note in specprop description)
sp.ent: spectral entropy
sfm: spectral flatness
mode: mode frequency
centroid: frequency centroid (see specprop)
meanfun: mean fundamental frequency measured across acoustic signal
minfun: minimum fundamental frequency measured across acoustic signal
maxfun: maximum fundamental frequency measured across acoustic signal
meandom: mean of dominant frequency measured across acoustic signal
mindom: minimum of dominant frequency measured across acoustic signal
maxdom: maximum of dominant frequency measured across acoustic signal
dfrange: range of dominant frequency measured across acoustic signal
modindx: modulation index

Preprocessing the Data

Run the preprocessing scripts

Modeling

Run the algorithms (Logistic, Decision Tree, SVM, Random Forest)

Feed Live Data

Record the sound (Male or Female). Covert it into a .wav file.

Create 2 folders Male and Female in your default working directory for R and paste the wav files in respective folders.

Run the R code – It will output the csv file with 20 features.

Final Modeling

Read the csv file in jupyter after heading “Prediction using real time voice samples”

Run the algorithms and it will predict the voice samples whether its Male or Female in 0’s and 1’s form