AhmedHamdy2b/gender-predection-py-voice AhmedHamdy2b/gender-predection-py-voice

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AhmedHamdy2b/gender-predection-py-voice
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About Dataset Voice Gender Gender Recognition by Voice and Speech Analysis This database 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). The Dataset The following acoustic properties of each voice are measured and included within the CSV: 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 label: male or female

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