ソフトウェアエンジニアの技術ブログ：Software engineer tech blog

短時間フーリエ変換、MFCCを利用する

import numpy as np
import librosa
import librosa.display
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import svm
from scipy import fftpack

# 音声データを読み込む
speakers = {'kirishima' : 0, 'suzutsuki' : 1, 'belevskaya' : 2}

# 特徴量を返す
def get_feat(file_name):
    a, sr = librosa.load(file_name)
    y = np.abs(librosa.stft(a))
    plt.figure(figsize=(10, 4))
    librosa.display.specshow(librosa.amplitude_to_db(y, ref=np.max), y_axis='log', x_axis='time', sr=sr)
    plt.colorbar(format='%+2.0fdB')
    plt.tight_layout()
    return y

# 特徴量と分類のラベル済みのラベルの組を返す
def get_data(dir_name):
    data_X = []
    data_y = []
    for file_name in sorted(os.listdir(path=dir_name)):
        print("read: {}".format(file_name))
        speaker = file_name[0:file_name.index('_')]
        data_X.append(get_feat(os.path.join(dir_name, file_name)))
        data_y.append((speakers[speaker], file_name))
        
    return (np.array(data_X), np.array(data_y))

# data_X, data_y = get_data('voiceset')
get_feat('sample/hi.wav')
get_feat('sample/lo.wav')

speakers = {'kirishima' : 0, 'suzutsuki' : 1, 'belevskaya' : 2}

# 特徴量を返す
def get_feat(file_name):
    a, sr = librosa.load(file_name)
    y = np.abs(librosa.stft(a))
#     plt.figure(figsize=(10, 4))
#     librosa.display.specshow(librosa.amplitude_to_db(y, ref=np.max), y_axis='log', x_axis='time', sr=sr)
#     plt.colorbar(format='%+2.0fdB')
#     plt.tight_layout()
    return y

# 特徴量と分類のラベル済みのラベルの組を返す
def get_data(dir_name):
    data_X = []
    data_y = []
    for file_name in sorted(os.listdir(path=dir_name)):
        print("read: {}".format(file_name))
        speaker = file_name[0:file_name.index('_')]
        data_X.append(get_feat(os.path.join(dir_name, file_name)))
        data_y.append((speakers[speaker], file_name))
        
    return (data_X, data_y)

data_X, data_y = get_data('voiceset')

train_X, test_X, train_y, test_y = train_test_split(data_X, data_y, random_state=11813)
print("{} -> {}, {}".format(len(data_X), len(train_X), len(test_X)))


def predict(X):
    result = clf.predict(X.T)
    return np.argmax(np.bincount(result))

ok_count = 0

for X, y in zip(test_X, test_y):
    actual = predict(X)
    expected = y[0]
    file_name = y[1]
    ok_count += 1 if actual == expected else 0
    result = 'o' if actual == expected else 'x'
    print("{} file: {}, actual: {}, expected: {}".format(result, file_name, actual, expected))

print("{}/{}".format(ok_count, len(test_X)))

MFCC

def get_feat(file_name):
    a, sr = librosa.load(file_name)
    y = librosa.feature.mfcc(y=a, sr=sr)
#     plt.figure(figsize=(10, 4))
#     librosa.display.specshow(librosa.amplitude_to_db(y, ref=np.max), y_axis='log', x_axis='time', sr=sr)
#     plt.colorbar(format='%+2.0fdB')
#     plt.tight_layout()
    return y

o file: suzutsuki_b06.wav, actual: 1, expected: 1
o file: kirishima_04_su.wav, actual: 0, expected: 0
o file: kirishima_c01.wav, actual: 0, expected: 0
o file: belevskaya_b04.wav, actual: 2, expected: 2
o file: belevskaya_b14.wav, actual: 2, expected: 2
o file: kirishima_b04.wav, actual: 0, expected: 0
o file: suzutsuki_b08.wav, actual: 1, expected: 1
o file: belevskaya_b07.wav, actual: 2, expected: 2
o file: suzutsuki_b03.wav, actual: 1, expected: 1
o file: belevskaya_b10.wav, actual: 2, expected: 2
o file: kirishima_b01.wav, actual: 0, expected: 0
o file: belevskaya_07_su.wav, actual: 2, expected: 2
12/12

MFCC凄すぎんだろこれ

元の波形を周波数ごとに分ける -> 周波数ごとに集計したもの:パワースペクトル
時間を考慮せず、周波数に対応する数値として捉える

import numpy as np
import librosa
import librosa.display
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import svm
from scipy import fftpack

# 音声データを読み込む
speakers = {'kirishima' : 0, 'suzutsuki' : 1, 'belevskaya' : 2}

# 特徴量を返す
def get_feat(file_name):
    a, sr = librosa.load(file_name)
    fft_wave = fftpack.rfft(a, n=sr)
    fft_freq = fftpack.rfftfreq(n=sr, d=1/sr)
    y = librosa.amplitude_to_db(fft_wave, ref=np.max)
    plt.plot(fft_freq, y)
    plt.show()
    return y

# 特徴量と分類のラベル済みのラベルの組を返す
def get_data(dir_name):
    data_X = []
    data_y = []
    for file_name in sorted(os.listdir(path=dir_name)):
        print("read: {}".format(file_name))
        speaker = file_name[0:file_name.index('_')]
        data_X.append(get_feat(os.path.join(dir_name, file_name)))
        data_y.append((speakers[speaker], file_name))
        
    return (np.array(data_X), np.array(data_y))

get_feat('sample/hi.wav')
get_feat('sample/lo.wav')

横軸が周波数
hi

low

# 教師データとテストデータに分ける
train_X, test_X, train_y, test_y = train_test_split(data_X, data_y, random_state=813)
print("{} -> {}, {}".format(len(data_X), len(train_X), len(test_X)))

clf = svm.SVC(gamma=0.0000001, C=10)
clf.fit(train_X, train_y.T[0])

ok_count = 0

for X, y in zip(test_X, test_y):
    actual = clf.predict(np.array([X]))[0]
    expected = y[0]
    file_name = y[1]
    ok_count += 1 if actual == expected else 0
    result = 'o' if actual == expected else 'x'
    print("{} file: {}, actual: {}, expected: {}".format(result, file_name, actual, expected))

print("{}/{}".format(ok_count, len(test_X)))

o file: belevskaya_b11.wav, actual: 2, expected: 2
o file: kirishima_c01.wav, actual: 0, expected: 0
x file: kirishima_c09.wav, actual: 2, expected: 0
x file: kirishima_04_su.wav, actual: 2, expected: 0
o file: belevskaya_b14.wav, actual: 2, expected: 2
o file: kirishima_b07.wav, actual: 0, expected: 0
x file: suzutsuki_b06.wav, actual: 2, expected: 1
x file: kirishima_c02.wav, actual: 2, expected: 0
o file: kirishima_b03.wav, actual: 0, expected: 0
o file: suzutsuki_b08.wav, actual: 1, expected: 1
o file: suzutsuki_b02.wav, actual: 1, expected: 1
o file: kirishima_b05.wav, actual: 0, expected: 0
8/12

精度が上がっている

import librosa
import librosa.display
import matplotlib.pyplot as plt

a, sr = librosa.load('voiceset/kirishima_b01.wav')
librosa.display.waveplot(a, sr)

print(a)
print(len(a))
print(sr)

print(a)
print(len(a))
print(sr)
print(a)
print(len(a))
print(sr)
[ 1.3803428e-06 -2.3314392e-06 7.8938438e-06 … 0.0000000e+00
0.0000000e+00 0.0000000e+00]
132300
22050 // 波形のデータが1秒間に幾つの振幅を持つか

### 高音と低音の比較

a, sr = librosa.load('sample/hi.wav')
librosa.display.waveplot(a, sr)
plt.show()

a, sr = librosa.load('sample/lo.wav')
librosa.display.waveplot(a, sr)
plt.show()

highはlowよりも細かく振動している
振動数が多いと音が高くなる傾向にある
この特性を元に、SVNに与えて話者認識を行う

import numpy as np
import librosa
import librosa.display
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import svm

dir_name = 'voiceset'
for file_name in sorted(os.listdir(path=dir_name)):
    print("read: {}".format(file_name))
    a, sr = librosa.load(os.path.join(dir_name, file_name))
    print(a.shape)
    librosa.display.waveplot(a, sr)
    plt.show()

同じ人の音声でも全く異なる波形になる

speakers = {'kirishima': 0, 'suzutsuki': 1, 'belevskaya': 2}


def get_data(dir_name):
    data_X = []
    data_y = []
    for file_name in sorted(os.listdir(path=dir_name)):
        print("read: {}".format(file_name))
        a, sr = librosa.load(os.path.join(dir_name, file_name))
        print(a.shape)
        speaker = file_name[0:file_name.index('_')]
        data_X.append(a)
        data_y.append((speakers[speaker], file_name))
    
    return (np.array(data_X), np.array(data_y))
    
data_X, data_y = get_data("voiceset")

SVMに学習させるには、要素数を同じ数に揃えなければならない

speakers = {'kirishima': 0, 'suzutsuki': 1, 'belevskaya': 2}

def get_feat(file_name):
    a, sr = librosa.load(file_name)
    return a[0:5000]

def get_data(dir_name):
    data_X = []
    data_y = []
    for file_name in sorted(os.listdir(path=dir_name)):
        print("read: {}".format(file_name))
        speaker = file_name[0:file_name.index('_')]
        data_X.append(get_feat(os.path.join(dir_name, file_name)))
        data_y.append((speakers[speaker], file_name))
    
    return (np.array(data_X), np.array(data_y))
    
data_X, data_y = get_data("voiceset")

print("====data_X====")
print(data_X.shape)
print(data_X)
print("====data_y====")
print(data_y.shape)
print(data_y)

教師データとテストデータに分割する

train_X, test_X, train_y, test_y = train_test_split(data_X, data_y, random_state=11813)
print("{}->{}, {}".format(len(data_X), len(train_X),len(test_X)))

cvmで学習

clf = svm.SVC(gamma=0.0001, C=1)
clf.fit(train_X, train_y.T[0])

SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=’ovr’, degree=3, gamma=0.0001, kernel=’rbf’,
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)

テストデータの分類

clf.predict(np.array([test_X[0]]))

ok_count = 0

for X, y in zip(test_X, test_y):
    actual = clf.predict(np.array([X]))[0]
    expected = y[0]
    file_name = y[1]
    ok_count += 1 if actual == expected else 0
    result = 'o' if actual == expected else 'x'
    print("{} file: {}, actual:{}, expected: {}".format(result, file_name, actual, expected))
    
print("{}/{}".format(ok_count, len(test_X)))

x file: suzutsuki_b06.wav, actual:2, expected: 1
x file: kirishima_04_su.wav, actual:2, expected: 0
x file: kirishima_c01.wav, actual:2, expected: 0
o file: belevskaya_b04.wav, actual:2, expected: 2
o file: belevskaya_b14.wav, actual:2, expected: 2
x file: kirishima_b04.wav, actual:2, expected: 0
x file: suzutsuki_b08.wav, actual:2, expected: 1
o file: belevskaya_b07.wav, actual:2, expected: 2
x file: suzutsuki_b03.wav, actual:2, expected: 1
o file: belevskaya_b10.wav, actual:2, expected: 2
x file: kirishima_b01.wav, actual:2, expected: 0
o file: belevskaya_07_su.wav, actual:2, expected: 2
5/12

予測の精度を上げる必要がある