File:Bark scale approximation.png
Summary
| Description |
English: The comparison of the several Bark scale approximation models. |
| Date | |
| Source | Own work |
| Author | Kirlf |
| PNG development | |
| Source code | Python codeimport numpy as np
import matplotlib.pyplot as plt
#Frequency array between 0 and 20000 Hz in 1000 steps:
f = np.linspace(0,20000,1000)
zw = 13*np.arctan(0.00076*f) + 3.5*np.arctan((f/7500)**2)
tr = (26.81*f / (1960 + f)) - 0.53
sr = 7*np.arcsinh(f/650)
wsg = 6*np.arcsinh(f/600)
kov = 8.96*np.log(0.978 + 5*np.log(0.994 + ( (f + 75.4)/2173 )**1.347))
plt.figure(figsize=(10, 7), dpi=300)
plt.plot(f*1e-3,zw, label='Zwicker')
plt.plot(f*1e-3,tr, label='Traunmüller')
plt.plot(f*1e-3,sr, label='Schroeder')
plt.plot(f*1e-3,wsg, label='Wang, Sekey & Gersho')
plt.plot(f*1e-3,kov, label='Kovalchuk')
plt.plot(np.array([100,1270,2700,6400,9500,15500])*1e-3,[1,10,15,20,22,24],\
'ro', label='Points from the Bark scale')
plt.xlabel('Frequency in kHz')
plt.ylabel('Frequency in Bark')
plt.title('Approximation of the Bark Scale')
plt.legend()
plt.grid()
plt.savefig('Results.png')
|
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