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for pitch, f1, f2, f3, tilt + """ + if _kde_functions: + return _kde_functions + + data: dict[Feature, dict[Gender, list[float]]] = {**json.loads(Path('results/frequency-data.json').read_text()), + **json.loads(Path('results/tilt-data.json').read_text())} + + # Lowercase keys + data = {k.lower(): data[k] for k in data} + + # Fit KDE functions + for feature in data: + _kde_functions[feature] = {} + for gender in data[feature]: + kde = gaussian_kde(data[feature][gender], 'scott') + _kde_functions[feature][gender] = kde + + return _kde_functions + + +def calculate_feature_means(audio: Sound) -> dict[Feature, float]: + s = calculate_freq_statistics(calculate_freq_info(audio)) + return {'pitch': s.pitch.mean, 'f1': s.f1.mean, 'f2': s.f2.mean, 'f3': s.f3.mean, 'tilt': tilt(audio)} + + +def _calculate_fem_prob(feature: Feature, value: float) -> float: + """ + Calculate probability of a feature sounding feminine + + :return: Ratio between 0 and 1 + """ + f = load_kde()[feature]['f'].evaluate([value])[0] + m = load_kde()[feature]['m'].evaluate([value])[0] + return f / (f + m) + + +def calculate_feature_classification(audio: Sound): + """ + Run statistical classification based on kernel density estimation. + + :param audio: Audio + :return: Statistical results {'means': {'pitch': ..., 'f1': ...}, 'fem_prob': {'pitch': ..., 'f1': ...}} + """ + means = calculate_feature_means(audio) + fem_prob = {feature: _calculate_fem_prob(feature, means[feature]) for feature in means} + return {'means': means, 'fem_prob': fem_prob} diff --git a/src/statistics.py b/src/statistics.py index e527f19..ecbb401 100644 --- a/src/statistics.py +++ b/src/statistics.py @@ -280,7 +280,6 @@ def collect_visualize_freq(): df = pd.DataFrame({headers[i]: f_means[:, i] for i in range(4)}) dm = pd.DataFrame({headers[i]: m_means[:, i] for i in range(4)}) args = dict(orient='h', scale='width', inner='quartile', linewidth=0.5) - sns.histplot() sns.violinplot(data=df, color=COLOR_PINK, **args) sns.violinplot(data=dm, color=COLOR_BLUE, **args) [c.set_alpha(0.7) for c in ax.collections] @@ -346,7 +345,7 @@ def collect_visualize_tilt(): plt.show() # Write JSON - data = {'f': f_means.tolist(), 'm': m_means.tolist()} + data = {'tilt': {'f': f_means.tolist(), 'm': m_means.tolist()}} Path('results/tilt-data.json').write_text(json.dumps(data), 'utf-8') @@ -364,7 +363,7 @@ if __name__ == '__main__': # call_id_vox_celeb(combine_id_freq) # 3. Collect statistics and draw visualizations - collect_visualize_freq() + # collect_visualize_freq() ########### # 1. Compute and save all the spectral tilt for vox1