Leahnaya/SpeechGenderAnalysis
1
0
Fork 0
mirror of https://github.com/hykilpikonna/SpeechGenderAnalysis.git synced 2026-04-25 15:58:12 -05:00
Code Issues Actions Packages Projects Releases Wiki Activity

[+] Added legend

Browse Source
This commit is contained in:
wuliaozhiji 2022-03-14 10:44:50 -04:00
parent dcc8cf5efc
commit 23a0182ef3
2 changed files with 50 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
Distribution.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 45 KiB

70
src/formant.py
View File

@ -16,9 +16,11 @@ import pandas as pd
import parselmouth
import tqdm
import seaborn as sns
from matplotlib.patches import Patch
ASAB = Literal['f', 'm']
COLOR_PINK = '#F5A9B8'
COLOR_BLUE = '#5BCEFA'
def calculate_freq_info(audio: parselmouth.Sound, show_plot=False) -> numpy.ndarray:
@ -153,6 +155,27 @@ class Statistics:
n: int
def calc_col_stats(col: np.ndarray) -> Statistics:
"""
Compute statistics for a data column
:param col: Input column (tested on 1D array)
:return: Statistics
"""
q1 = np.quantile(col, 0.25)
q3 = np.quantile(col, 0.75)
return Statistics(
float(np.mean(col)),
float(np.median(col)),
float(q1),
float(q3),
float(q3 - q1),
float(np.min(col)),
float(np.max(col)),
len(col)
)
def calculate_statistics(arr: np.ndarray) -> FrequencyStats:
"""
Calculate frequency data array statistics
@ -160,20 +183,6 @@ def calculate_statistics(arr: np.ndarray) -> FrequencyStats:
:param arr: n-by-4 Array from calculate_freq_info
:return: Statistics
"""
def calc_col_stats(col: np.ndarray) -> Statistics:
q1 = np.quantile(col, 0.25)
q3 = np.quantile(col, 0.75)
return Statistics(
float(np.mean(col)),
float(np.median(col)),
float(q1),
float(q3),
float(q3 - q1),
float(np.min(col)),
float(np.max(col)),
len(arr)
)
result = [calc_col_stats(arr[:, i]) for i in range(0, 4)] + \
[calc_col_stats(np.divide(arr[:, i], arr[:, 0])) for i in range(1, 4)]
@ -219,7 +228,7 @@ def collect_statistics():
stats_list.append((jsonpickle.decode(stats_dir.read_text()), agab[id]))
# Get AFAB and AMAB means
headers = ['Pitch (Fundamental Frequency)', 'Formant F1', 'Formant F2', 'Formant F3', 'F1 Ratio', 'F2 Ratio', 'F3 Ratio']
headers = ['Pitch\n(Fundamental\nFrequency)', 'Formant F1', 'Formant F2', 'Formant F3', 'F1 Ratio', 'F2 Ratio', 'F3 Ratio']
f_means = np.array([[t.mean for t in [s.pitch, s.f1, s.f2, s.f3, s.f1ratio, s.f2ratio, s.f3ratio]]
for s, ag in stats_list if ag == 'f'])
m_means = np.array([[t.mean for t in [s.pitch, s.f1, s.f2, s.f3, s.f1ratio, s.f2ratio, s.f3ratio]]
@ -245,7 +254,19 @@ def collect_statistics():
sns.set_theme(style="ticks")
fig, ax = subplots(figsize=(10, 5))
# ax.set_xscale('log')
#print(sns.load_dataset('tips'))
print("Pitch")
print(calc_col_stats(f_means[:, 0]))
print(calc_col_stats(m_means[:, 0]))
print("F1")
print(calc_col_stats(f_means[:, 1]))
print(calc_col_stats(m_means[:, 1]))
print("F2")
print(calc_col_stats(f_means[:, 2]))
print(calc_col_stats(m_means[:, 2]))
print("F3")
print(calc_col_stats(f_means[:, 3]))
print(calc_col_stats(m_means[:, 3]))
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)})
# data.boxplot()
@ -253,13 +274,22 @@ def collect_statistics():
# sns.boxplot(data=dm, orient='h', color='#5BCEFA', linewidth=0.5)
# sns.stripplot(x="distance", y="method", data=data, size=4, color=".3", linewidth=0)
args = dict(orient='h', scale='width', inner='quartile', linewidth=0.5)
sns.violinplot(data=df, color='#F5A9B8', **args)
sns.violinplot(data=dm, color='#5BCEFA', **args)
#dt=pd.DataFrame({"Female":df, "Male":dm})
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]
# Create legend
legend_elements = [
Patch(facecolor=COLOR_PINK, edgecolor='r', label='Feminine'),
Patch(facecolor=COLOR_BLUE, edgecolor='b', label='Masculine'),
]
plt.legend(handles=legend_elements)
ax.set_title("Distribution of Pitch and Formant Based on Gender")
ax.xaxis.grid(True)
ax.set_ylabel('')
ax.set_xlabel('Frequency (Hz)')
sns.despine(fig, ax)
plt.show()