from scipy.signal import filtfilt
from chapter2_基础.soundBase import *
from chapter3_分析实验.lpc import lpc_coeff
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
data, fs = soundBase('C6_1_y.wav').audioread()
N = len(data)
time = [i / fs for i in range(N)] # 设置时间
p = 12
ar, g = lpc_coeff(data, p)
ar[0] = 0
est_x = filtfilt(-ar, [1], data)
plt.subplot(2, 1, 1)
plt.plot(time, data, 'k')
plt.plot(time, est_x, 'c')
plt.title('LPC解码')
plt.legend(['信号', '解码信号'])
plt.subplot(2, 1, 2)
plt.plot(est_x - data)
plt.title('误差')
plt.savefig('images/LPC解码.png')
plt.close()
from scipy.io import wavfile
import matplotlib.pyplot as plt
from chapter3_分析实验.windows import *
from chapter3_分析实验.timefeature import *
from chapter2_基础.soundBase import *
data, fs, nbits = soundBase('C3_2_y.wav').audioread()
inc = 100
wlen = 200
win = hanning_window(wlen)
N = len(data)
time = [i / fs for i in range(N)]
EN = STEn(data, win, inc) # 短时能量
Mn = STMn(data, win, inc) # 短时平均幅度
Zcr = STZcr(data, win, inc) # 短时过零率
X = enframe(data, win, inc)
X = X.T
Ac = STAc(X)
Ac = Ac.T
Ac = Ac.flatten()
Amdf = STAmdf(X)
Amdf = Amdf.flatten()
fig = plt.figure(figsize=(14, 13))
plt.subplot(3, 1, 1)
from chapter2_基础.soundBase import *
from chapter3_分析实验.lpc import *
data, fs = soundBase('C3_5_y.wav').audioread()
L = 240
p = 12
x = data[8000:8000 + L]
ar, G = lpc_coeff(x, p)
nfft = 512
W2 = nfft // 2
m = np.array([i for i in range(W2)])
Y = np.fft.fft(x, nfft)
Y1 = lpcff(ar, W2)
plt.subplot(2, 1, 1)
plt.plot(x)
plt.subplot(2, 1, 2)
plt.plot(m, 20 * np.log(np.abs(Y[m])))
plt.plot(m, 20 * np.log(np.abs(Y1[m])))
plt.savefig('images/lpcff.png')
plt.close()
from chapter2_基础.soundBase import *
from chapter4_特征提取.pitch_detection import *
data, fs = soundBase('C4_2_y.wav').audioread()
data -= np.mean(data)
data /= np.max(np.abs(data))
wlen = 320
inc = 80
N = len(data)
time = [i / fs for i in range(N)]
T1 = 0.05
# 4.2.1
voiceseg, vosl, SF, Ef = pitch_vad(data, wlen, inc, T1)
fn = len(SF)
frameTime = FrameTimeC(fn, wlen, inc, fs)
plt.figure(figsize=(14, 8))
plt.subplot(5, 1, 1)
plt.plot(time, data)
plt.subplot(5, 1, 2)
plt.plot(frameTime, Ef)
for i in range(vosl):
plt.subplot(5, 1, 2)
plt.plot(frameTime[voiceseg[i]['start']], Ef[voiceseg[i]['start']], '.k')
plt.plot(frameTime[voiceseg[i]['end']], Ef[voiceseg[i]['start']], 'or')
plt.legend(['能熵比', 'start', 'end'])
# 4.2.3
voiceseg, vsl, SF, Ef, period = pitch_Ceps(data, wlen, inc, T1, fs, miniL=10)
else:
xx[j] = x[j] // 2 * 2 + 1
return xx, m_len
def extract_message(x, m_len, nBits=1):
if nBits != 1:
exit('Only nBits=1 support now......')
meg = np.zeros((m_len, nBits))
for i in range(nBits):
for j in range(m_len):
meg[j, i] = x[j] % 2
return meg
data, fs, bits = soundBase('C8_1_y.wav').audioread(return_nbits=True)
data16 = (data + 1) * np.power(2, bits - 1)
nBits = 1
s = loadmat('C8_1_y.DAT')
x_embed, m_len = hide_message(data16, s['message'][0], 1)
meg_rec = extract_message(x_embed, m_len, 1)
plt.figure(figsize=(14, 12))
plt.subplot(3, 1, 1)
plt.plot(data16)
plt.subplot(3, 1, 2)
plt.plot(x_embed)
plt.subplot(3, 1, 3)
plt.plot(data16 - x_embed)
from chapter2_基础.soundBase import *
from chapter5_语音降噪.Wavelet import *
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
def awgn(x, snr):
snr = 10**(snr / 10.0)
xpower = np.sum(x**2) / len(x)
npower = xpower / snr
return x + np.random.randn(len(x)) * np.sqrt(npower)
data, fs = soundBase('C5_4_y.wav').audioread()
data -= np.mean(data)
data /= np.max(np.abs(data))
SNR = 5
N = len(data)
s = awgn(data, SNR)
time = [i / fs for i in range(N)] # 设置时间
wname = 'db7'
jN = 6
res_s = Wavelet_Soft(s, jN, wname)
res_d = Wavelet_Hard(s, jN, wname)
res_hs = Wavelet_hardSoft(s, jN, wname)
res_a = Wavelet_average(s, jN, wname)
plt.figure(figsize=(14, 10))
from chapter3_分析实验.windows import *
from chapter3_分析实验.timefeature import *
from chapter2_基础.soundBase import *
from chapter3_分析实验.倒谱计算 import *
data, fs = soundBase('C3_4_y_1.wav').audioread()
nlen = 1000
y = data[:nlen]
N = 1024
time = [i / fs for i in range(nlen)]
z = cceps(y)
zr = rcceps(y)
yy = icceps(z)
plt.subplot(4, 1, 1)
plt.plot(time, y)
plt.title('原始信号')
plt.subplot(4, 1, 2)
plt.plot(time, z)
plt.title('复倒谱')
plt.subplot(4, 1, 3)
plt.plot(time, zr)
plt.title('实倒谱')
plt.subplot(4, 1, 4)
plt.plot(time, yy)
plt.title('重构信号')
plt.savefig('images/倒谱.png')
plt.close()
scores1 = np.zeros(N)
scores2 = np.zeros(N)
scores3 = np.zeros(N)
for i in range(N):
scores1[i] = myDCW(CMN(features['p1_{}'.format(i)]), r)
scores2[i] = myDCW(CMN(features['p2_{}'.format(i)]), r)
scores3[i] = myDCW(CMN(features['p2_{}'.format(i)]), r)
return scores1, scores2, scores3
if __name__ == '__main__':
# 制作模板集
features = {}
for i in range(10):
data, fs, bits = soundBase('p1/{}.wav'.format(i)).audioread()
speechIn1 = my_vad(data)
fm = mfccf(12, speechIn1, fs)
features['p1_{}'.format(i)] = fm
for i in range(10):
data, fs, bits = soundBase('p2/{}.wav'.format(i)).audioread()
speechIn1 = my_vad(data)
fm = mfccf(12, speechIn1, fs)
features['p2_{}'.format(i)] = fm
for i in range(10):
data, fs, bits = soundBase('p3/{}.wav'.format(i)).audioread()
speechIn1 = my_vad(data)
fm = mfccf(12, speechIn1, fs)
features['p3_{}'.format(i)] = fm
soundBase('mysound.wav').audiorecorder(rate=16000, channels=1)
from chapter2_基础.soundBase import *
from chapter3_分析实验.dct import *
from chapter3_分析实验.mel import *
data, fs, _ = soundBase('C3_4_y_4.wav').audioread()
wlen = 200
inc = 80
num = 8
data = data / np.max(data)
mfcc = Nmfcc(data, fs, num, wlen, inc)
else:
xx[j] = x[j] // 2 * 2 + 1
return xx, m_len
def extract_message(x, m_len, nBits=1):
if nBits != 1:
exit('Only nBits=1 support now......')
meg = np.zeros((m_len, nBits))
for i in range(nBits):
for j in range(m_len):
meg[j, i] = x[j] % 2
return meg
data, fs, bits = soundBase('C8_1_y.wav').audioread()
data16 = (data + 1) * np.power(2, bits - 1)
nBits = 1
s = loadmat('C8_1_y.DAT')
x_embed, m_len = hide_message(data16, s['message'][0], 1)
meg_rec = extract_message(x_embed, m_len, 1)
plt.figure(figsize=(14, 12))
plt.subplot(3, 1, 1)
plt.plot(data16)
plt.subplot(3, 1, 2)
plt.plot(x_embed)
plt.subplot(3, 1, 3)
plt.plot(data16 - x_embed)
from chapter3_分析实验.timefeature import *
from chapter7_语音合成.flipframe import *
from chapter3_分析实验.C3_1_y_1 import enframe
from chapter3_分析实验.lpc import lpc_coeff
from chapter4_特征提取.共振峰估计 import *
from chapter4_特征提取.pitch_detection import *
from chapter7_语音合成.myfilter import *
from scipy.signal import lfilter
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
data, fs = soundBase('C7_3_y.wav').audioread()
data -= np.mean(data)
data /= np.max(np.abs(data))
data = lfilter([1, -0.99], 1, data)
N = len(data)
time = [i / fs for i in range(N)] # 设置时间
wlen = 240
inc = 80
overlap = wlen - inc
n2 = [i for i in range(wlen // 2)]
w1 = [i / overlap for i in range(overlap)]
w2 = [i / overlap for i in range(overlap - 1, -1, -1)]
wnd = np.hamming(wlen)
X = enframe(data, wnd, inc)
fn = X.shape[0]
Etmp = np.sum(np.power(X, 2), axis=1)
from chapter2_基础.soundBase import *
from chapter11_说话人识别.VQ import *
from chapter10_语音识别.DTW.DCW import mfccf
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
k = 8
N = 4
## 生成book
u = np.zeros(N)
for i in range(1, N + 1):
s = 'VQ_data/SX' + str(i) + '.WAV'
# s = 'VQ_data/mysound.WAV'
data, fs, bits = soundBase(s).audioread()
data /= np.max(data)
mel = mfccf(12, data, fs)
v = lbg(mel.T, k)
u[i] = v
## 识别过程
M = 4 # 每个人有M个待识别的样本
l = 5
# 这部分需要用新的语音信号对于MATLAB调试查看结果
for iii in range(l):
for i in range(M):
Dstu = np.zeros(N)
s = 'VQ_data/TX{}_{}.wav'.format(iii, i)
data, fs, bits = soundBase(s).audioread()