http://www.primaryobjects.com/2016/06/22/identifying-the-gender-of-a-voice-using-machine-learning/
Decision Tree plotting - http://scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html
PCA - http://setosa.io/ev/principal-component-analysis/
http://scikit-learn.org/stable/modules/ensemble.html
Column Names - "meanfreq", "sd", "median", "Q25", "Q75", "IQR", "skew", "kurt", "sp.ent", "sfm", "mode", "centroid", "meanfun", "minfun", "maxfun", "meandom", "mindom", "maxdom", "dfrange", "mdindx", "label"
- duration: length of signal
- 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)
- peakf: peak frequency (frequency with highest energy)
- meanfun: average of fundamental frequency measured across acoustic signal
- minfun: minimum fundamental frequency measured across acoustic signal
- maxfun: maximum fundamental frequency measured across acoustic signal
- meandom: average 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. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range