mirror of https://github.com/drowe67/phasenn.git
81 lines
2.0 KiB
Python
Executable File
81 lines
2.0 KiB
Python
Executable File
#!/usr/bin/python3
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# plot_n0.py
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#
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# David Rowe Dec 2019
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# Plot n0 estimates on top of speech waveforms for a few test frames
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# to see if n0 estimation is working.
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import numpy as np
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import sys
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import matplotlib.pyplot as plt
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from scipy import signal
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import codec2_model
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# constants
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N = 80 # number of time domain samples in frame
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width = 256
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Fs = 8000
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# read in model file records
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Wo, L, A, phase, voiced = codec2_model.read(sys.argv[1])
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nb_samples = Wo.size;
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amp = 20.0*np.log10(A+1E-6)
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# read in n0 estimates
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n0_est = np.loadtxt(sys.argv[2])
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print(n0_est[:20])
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print("removing linear phase component....")
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phase_n0_removed = np.zeros((nb_samples, width))
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for i in range(nb_samples):
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for m in range(1,L[i]+1):
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a = n0_est[i]*Wo[i]*m
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phase_n0_removed[i,m] = np.angle(np.exp(1j*a))
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# TODO: some how choose random set up vectors to plot. Want them above a certain level, and mix of V and UV
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frame = range(20,32)
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# synthesise time domain signal
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def sample_time(r):
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s = np.zeros(2*N);
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for m in range(1,L[r]+1):
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s = s + A[r,m]*np.cos(m*Wo[r]*range(2*N) + phase[r,m])
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return s
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plot_en = 1;
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if plot_en:
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plt.figure(1)
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plt.title('Amplitudes Spectra')
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for r in range(12):
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plt.subplot(3,4,r+1)
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f = frame[r];
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plt.plot(amp[f,1:L[f]],'g')
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plt.ylim(-10,60)
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plt.show(block=False)
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plt.figure(2)
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plt.title('Phase Spectra')
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for r in range(12):
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plt.subplot(3,4,r+1)
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f = frame[r];
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plt.plot(phase_n0_removed[f,1:L[f]]*180/np.pi,'r')
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plt.plot(phase[f,1:L[f]]*180/np.pi,'g')
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plt.ylim(-180,180)
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plt.show(block=False)
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plt.figure(3)
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plt.title('Time Domain')
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for r in range(12):
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plt.subplot(3,4,r+1)
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f = frame[r];
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s = sample_time(f)
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plt.plot(s,'g')
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mx = np.max(s)
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plt.plot([n0_est[f],n0_est[f]], [-mx/2,mx/2],'b')
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plt.show(block=False)
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# click on last figure to close all and finish
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plt.waitforbuttonpress(0)
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plt.close()
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