In this project, it was used different Machine Learning models to identify the gender of a voice (Female or Male) based on some specific speech and voice attributes.
Image obtained from GIF By Scenes.
Models implemented by Anne Livia.
- This dataset was obtained from Kaggle on this link by Kory Becker and was created to identify a voice as male or female, based upon acoustic properties of the voice and speech.
- The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).
- 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: 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
- label: male or female
- Python
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn
- XGBoost
-
precision recall f1-score support 0 0.96 0.97 0.97 314 1 0.97 0.96 0.97 320 accuracy 0.97 634 macro avg 0.97 0.97 0.97 634 weighted avg 0.97 0.97 0.97 634 -
precision recall f1-score support 0 0.98 0.98 0.98 314 1 0.98 0.98 0.98 320 accuracy 0.98 634 macro avg 0.98 0.98 0.98 634 weighted avg 0.98 0.98 0.98 634 -
precision recall f1-score support 0 0.98 0.99 0.99 314 1 0.99 0.98 0.99 320 accuracy 0.99 634 macro avg 0.99 0.99 0.99 634 weighted avg 0.99 0.99 0.99 634 -
precision recall f1-score support 0 0.98 0.99 0.99 314 1 0.99 0.98 0.99 320 accuracy 0.99 634 macro avg 0.99 0.99 0.99 634 weighted avg 0.99 0.99 0.99 634