Pengenalan Karakteristik Suara Menggunakan Mel Frequency Cepstrum Coefficients (Mfcc) Pada Sistem Pengenalan Pembicara (Speaker Recognition Sistem)

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38 3.01883 2.536029 2.550253

39 2.947809 2.437459 2.76519

40 3.015703 2.801276 2.909272

41 2.883735 2.832198 2.900184

42 2.804869 2.72857 2.77687

43 3.049127 2.801163 2.81652

44 3.074309 2.708278 2.919326

45 2.944557 2.586678 2.947231

46 2.97083 2.467131 2.929003

47 3.104349 2.668157 2.929228

48 3.137422 2.693482 2.903351

49 3.031652 2.831464 2.848655

50 3.070853 2.854678 2.65543

51 2.898006 2.901801 2.553883

52 3.008363 3.038533 2.592927

53 2.894335 3.036239 2.744685

54 2.800461 2.967859 2.861456

55 2.914787 2.907526 2.800693

56 2.921724 2.855878 2.754391

57 2.746666 2.902099 2.649201

58 2.869858 2.743736 2.877687

59 2.918968 2.689295 2.802808

60 2.834738 2.560883 2.655405

61 2.729312 2.603151 2.762717

62 2.924216 2.725129 2.804772

63 2.842529 2.755811 2.727388

64 2.909955 2.754071 2.657313

65 2.845307 2.67055 2.793663

66 2.736722 2.669645 2.610461

67 2.338206 2.685237 1.74934

68 2.213404 2.662591 2.114093

69 1.834132 2.646394

70 2.536849

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LAMPIRAN B

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LAMPIRAN C

Sintaks Program MATLAB rekaman suara

% Record your voice for 2 second. rec0bj = audiorecorder;

disp('Start speaking.') recordblocking(rec0bj, 5); disp('End of Recording.');

% Play back the recording. play(rec0bj);

% store data in double-precision array. myRecording = getaudiodata(rec0bj);

wavwrite(myRecording,'suaraujipertama.wav');

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LAMPIRAN D

Sintaks Program MATLAB pengambilan data

%Data Awal Speech Continuous

[data, Fs, nbits, opts] = wavread('suaraujipertama.wav'); siz = wavread('suaraujipertama.wav','size');

%xlswrite('zero2.xlsx',data);

nbFrames = ceil((length(data)-N)/M); Frames = zeros(nbFrames+1,N);

lastLength = length(data)- nbFrames*M;

temp(1:lastLength) = data(nbFrames*M+1:(nbFrames*M +1 + lastLength-1)); % Hamming Window

w = hamming(nbSamples);

Windows = zeros(nbFrames,nbSamples); for i = 1:nbFrames

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%% Mel-frequency Wrapping.. % (a) Calculate Power spectrum.. PowSpecs = abs(fourir).^2; PowSpecs = PowSpecs(1:NN-1,:);

% (b) Mel filter generation

nof_c = 20; % Number of channels..

% Convert to frequency scale..

fcenters = 700*((10.^(melcenters./2595))-1); centerf = round(fcenters./df);

startf = [1,centerf(1:nof_c-1)]; stopf = [centerf(2:nof_c),Nmax]; W = zeros(nof_c,Nmax);

% Making filter.. for i = 1:nof_c

increment = 1.0/(centerf(i)-startf(i)); for j = startf(i):centerf(i)

W(i,j) = (j-startf(i))*increment; end

decrement = 1.0/(stopf(i)-centerf(i)); for j = centerf(i):stopf(i)

W(i,j) = (j-centerf(i))*decrement;

% (c) Apply mel filters to Power spectrum coeffs.. melPowSpecs = W*PowSpecs;

% (d) MFCC calculations..

melCeps = dct(log(melPowSpecs)); melCeps(1,:) = [];

cepstrum = rceps(melCeps);

[long,coefficient] = size(cepstrum); x = icceps(long,coefficient);

z = max(sum(abs(cepstrum))); y = round(10000*z);

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plot(cepstrum);

title('After Cepstrum');

%Display if y == 66971

%fprintf('Nilai Koefisien Sinyal Suara\n');

fprintf('SINYAL SUARA MILIK SAUDARA WINDY\n');

msgbox('SINYAL SUARA MILIK SAUDARA WINDY','Success'); elseif y == 59399

fprintf('SINYAL SUARA MILIK SAUDARA A\n');

msgbox('SINYAL SUARA MILIK SAUDARA A','Success'); elseif y == 344028

fprintf('SINYAL SUARA MILIK SAUDARA B\n');

msgbox('SINYAL SUARA MILIK SAUDARA B','Success'); elseif y == 58991

fprintf('SINYAL SUARA MILIK SAUDARA C\n');

msgbox('SINYAL SUARA MILIK SAUDARA C','Success'); elseif y == 60540

fprintf('SINYAL SUARA MILIK SAUDARA D\n');

msgbox('SINYAL SUARA MILIK SAUDARA D','Success'); elseif y == 76828

fprintf('SINYAL SUARA MILIK SAUDARA E\n');

msgbox('SINYAL SUARA MILIK SAUDARA E','Success');