Saran - KLASIFIKASI SUARA DENGAN EKSTRAKSI CIRI MEL FREQUENCY CEPSTRAL COEFFICIENTS MENGGUNAKAN

BAB V PENUTUP

5.2 Saran

Beberapa saran untuk peneliti lainnya yang tertarik untuk mengembangkan penelitian terkait penggunaan data audio adalah sebagai berikut

1. Dalam ekstraksi ciri menggunakan metode MFCC agar dapat ditemukan metode optimasi parameter yang tepat dalam menentukan parameter-parameter yang optimal sehingga dapat dihasilkan data hasil ekstraksi yang lebih optimal pula.

2. Data yang digunakan pada penelitian selanjutnya dapat ditambahkan data suara dengan kebisingan seperti berada di keramaian atau dapat juga dengan mempertimbangkan jarak antara microphone dan partisipan serta faktor-faktro lain seperti jenis kelamin dan umur responden.

3. Pada tahap klasifikasi suara dapat diterapkan lagi penggunaan metode klasifikasi yang lainnya atau metode klasifikasi terbarukan agar dapat dibandingkan metode mana yang paling baik digunakan dalam mengklasifikasi data suara hasil ekstraksi ciri MFCC.

DAFTAR PUSTAKA

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http://sutikno.blog.undip.ac.id/files/2011/11/tutorial-svm.pdf. [Accessed 17 November 2020].

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Tokopedia Menggunakan Support Vector Machine," UIN Syarif Hidayatullah Jakarta, Jakarta, 2020.

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54 LAMPIRAN

A. Hasil Konversi Data Analog ke bentuk Digital

Hasil Konversi File Suara “Assalamualaikum”

0 1 2 3 … 44099 0 _-0,00655 _-0,01246 _-0,01002 _-0,00646 … 0,04037 1 _-0,00263 _-0,00438 _-0,00132 _-0,00725 … 0,00189 2 _-0,01220 _-0,01102 _-0,00158 _-0,00510 … 0,00138 3 _-0,01391 _-0,02107 _-0,02323 _-0,02793 … -0,00769 4 _0,00400 _0,00679 _0,00347 _-0,00159 … -0,00685 … … … … … 29 _0,00197 _-0,00469 _0,00387 _-0,00663 … -0,00092

Hasil Konversi File Suara “Astaghfirullah”

0 1 2 3 … 44099 0 _-0,00172 _-0,00370 _0,00180 _0,00129 … -0,12748 1 _-0,00578 _-0,00378 _-0,00193 _-0,00200 … -0,00264 2 _0,00000 _0,00000 _0,00000 _0,00000 … 0,00990 3 _0,00000 _0,00000 _0,00000 _0,00000 … -0,00354 4 _0,00411 _0,00061 _0,00210 _-0,00100 … 0,00408 … … … … … 29 _0,00744 _0,00710 _0,00380 _0,00472 … 0,00479

B. Ekstraksi Ciri Dengan Mel Frequency Ceptral Coefficients (MFCC) :

Importing modul import numpy

55 import scipy.io.wavfile

from scipy.fftpack import dct

import glob

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.svm import SVC

from sklearn.model_selection import GridSearchCV

Pre emphasis

def pre_emphasis(signal):

pre_emphasis = 0.97

signal_emphasis = numpy.append(signal[0], signal[1:] - pre_emphasis * signal[:-1])

return signal_emphasis

Frame Blocking

def framming(signal_emphasis, sample_rate):

frame_size=0.025

56 frame_length = frame_size * sample_rate

frame_step = frame_stride * sample_rate

signal_length = len(signal_emphasis)

frames_overlap = frame_length - frame_step

num_frames = numpy.abs(signal_length - frames_overlap) // numpy.abs(frame_length - frames_overlap)

rest_samples = numpy.abs(signal_length - frames_overlap) % numpy.abs(frame_length - frames_overlap)

pad_signal_length = int(frame_length - rest_samples)

z = numpy.zeros((pad_signal_length)) pad_signal = numpy.append(signal_emphasis, z) frame_length = int(frame_length) frame_step = int(frame_step) num_frames = int(num_frames)

indices = numpy.tile(numpy.arange(0, frame_length), (num_frames, 1))+ numpy.tile(numpy.arange(0, num_frames * frame_step,

frame_step),(frame_length, 1)).T

57 return frames, frame_length

Windowing

def windowing(frames, frame_length):

frames= frames*(numpy.hamming(frame_length))

return frames

Fast Fourier Transform (FFT) def fft(frames): NFFT = 512 mag_frames = numpy.absolute(numpy.fft.rfft(frames, NFFT)) pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2)) return pow_frames, NFFT Filter Bank

def filter_bank(pow_frames, sample_rate, NFFT):

nfilt = 40

low_freq_mel = 0

high_freq_mel = (2595 * numpy.log10(1 + (sample_rate / 2) / 700

mel_points = numpy.linspace(low_freq_mel, high_freq_mel, nfilt + 2)

bin = numpy.floor((NFFT + 1) * hz_points / sample_rate)

fbank = numpy.zeros((nfilt, int(numpy.floor(NFFT / 2 + 1))))

for m in range(1, nfilt + 1):

f_m_minus = int(bin[m - 1]) # left

f_m = int(bin[m]) # center

f_m_plus = int(bin[m + 1]) # right

for k in range(f_m_minus, f_m):

fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])

for k in range(f_m, f_m_plus):

fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])

filter_banks = numpy.dot(pow_frames, fbank.T)

filter_banks = 20 * numpy.log10(filter_banks) # dB

return(filter_banks)

Discrete Cosinous Transform & Cepstral Liftering def cepstral_liftering(filter_banks):

num_ceps = 12

cep_lifter= 11

59 (nframes, ncoeff) = mfcc.shape

n = numpy.arange(ncoeff)

lift = 1 + (cep_lifter / 2) * numpy.sin(numpy.pi * n / cep_lifter)

mfcc *= lift

mfcc -= (numpy.mean(mfcc, axis=0) + 1e-8)

return mfcc Ekstraksi Ciri MFCC def generate_features(): all_features=[] all_labels=[]

audios=['alhamdulillah', 'assalamualaikum', 'astaghfirullah','subhanallah', 'waalaikumsalam', ]

for audio in audios:

sound_files=glob.glob('audio/'+audio+'/*.wav')

print('processing %d audios in %s file... '% (len(sound_files), audio))

for f in sound_files:

sample_rate, signal = scipy.io.wavfile.read(f)

signal = signal[0:int(1*sample_rate), 0]

60 frames=framming(pre, sample_rate) window=windowing(frames[0], frames[1]) ff=fft(window) filter=filter_bank(ff[0], sample_rate, ff[1]) mfcc=cepstral_liftering(filter) mfcc=numpy.ndarray.flatten(mfcc) all_features.append(mfcc) all_labels.append(audio)

return all_features, all_labels

C. Hasil Ektraksi Ciri dengan MFCC

Hasil Ekstraksi Ciri MFCC Data Suara “Subhanallah”

0 1 2 3 … 1175 0 -38,337 -245,812 -138,206 169,163 … -4,088 1 -105,784 -324,813 -218,501 -98,019 … -11,022 2 -67,438 -108,481 -188,076 -21,469 … -4,994 3 3,164 -108,481 -188,796 286,928 … 1,661 4 -28,172 -53,643 -8,423 31,634 … 9,607 … … … … … 29 -13,319 -92,784 -174,765 89,633 … -6,039

Hasil Ekstraksi Ciri MFCC Data Suara “Waalaikumsalam”

0 1 2 3 … 1175

61 1 -136,464 -284,894 -12,566 54,861 … -7,651 2 1,558 140,450 292,509 144,821 … -11,666 3 -19,404 69,041 238,048 242,339 … -10,454 4 71,928 154,744 54,342 110,388 … -10,610 … … … … … 29 -8,349 39,595 154,606 133,131 … 4,333 D. Pembagian Data

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2,random_state=0)

E. Optimasi Parameter

parameters = [ {"n_estimators":[10,20,30,40], "max_depth":[4, 5, 10, 20, 100, 300, 500],"random_state":[0]}]

Rf = GridSearchCV(RandomForestClassifier(), parameters, cv=10, scoring = "accuracy")

Rf.fit(X_train, y_train)

print(Rf.best_estimator_)

print(Rf.best_score_)

k_range = list(range(1, 51))

weight_options = ['uniform', 'distance']

knn= GridSearchCV(KNeighborsClassifier(), prm_grid,cv=10, scoring='accuracy')

knn.fit(X_train, y_train)

print('Parameter terbaik:{}\nbest score:{}'.format(knn.best_params_, knn.best_score_))

svm= GridSearchCV(SVC(kernel='linear'),

param_grid = {'C': [0.1, 1, 10, 100, 1000]},cv=10, scoring='accuracy')

svm.fit(X_train, y_train)

print('Parameter terbaik:{}\nbest score:{}'.format(svm.best_params_, svm.best_score_))

kNN_= KNeighborsClassifier(n_neighbors= 1, weights= 'uniform')

svm_=SVC(kernel= 'linear', C= 0.1)

Rf_=RandomForestClassifier(max_depth=4, n_estimators=40, random_state=0)

Plotting akurasi dengan parameter terbaik model_ = ['kNN', 'SVM', "Rf"]

knn_score = cross_val_score(kNN_, X_train, y_train, cv=10, scoring='accuracy', n_jobs=-2, verbose=1)

svm_score = cross_val_score(svm_, X_train, y_train, cv=10, scoring='accuracy', n_jobs=-2, verbose=1)

Rf_score= cross_val_score(Rf_, X_train, y_train, cv=10, scoring='accuracy', n_jobs=-2, verbose=1)

score_ = [knn_score, svm_score, Rf_score]

data = {m:s for m,s in zip(model_, score_)}

for name in data.keys():

print("Accuracy %s: %0.2f (+/- %0.2f)" % (name, data[name].mean(), data[name].std() * 2)) sns.boxplot(data=pd.DataFrame(data), orient='h') plt.savefig("plot.png") F. Evaluasi Model svm_.fit(X_train, y_train) y_pred = svm_.predict(X_test) import sklearn sklearn.metrics.accuracy_score(y_test, y_pred)

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

print(confusion_matrix(y_test, y_pred))

print(classification_report(y_test, y_pred))

print('Akurasi : ', accuracy_score(y_test, y_pred))

kNN_.fit(X_train, y_train)

64 import sklearn

sklearn.metrics.accuracy_score(y_test, y_pred)

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

print(confusion_matrix(y_test, y_pred))

print(classification_report(y_test, y_pred))

print('Akurasi : ', accuracy_score(y_test, y_pred))

G. Confusion Matrix

from sklearn.metrics import plot_confusion_matrix

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 10))

plot_confusion_matrix(svm_, X_test, y_test, cmap=plt.cm.Blues, ax=ax)

plt.savefig('confusion matrix.png')

plt.show()

from sklearn.metrics import plot_confusion_matrix

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 10))

plot_confusion_matrix(kNN_, X_test, y_test, cmap=plt.cm.Blues, ax=ax)

plt.savefig('confusion matrix-knn.png')

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