Praktikum IV Pengukuran Sinyal

PRAKTIKUM IV

PEMROSESAN SINYAL

Nama : Muhammad Faisol Haq

NRP : 2408 100 010

I. Fungsi Window dan FilterKetik command line berikut ini

% Sampling frequency in HzFs = 16000; % contoh Rectangular and Hamming window, banyak fungsi window lainnyajendela1 = rectwin(51);jendela2 = hamming(51); % Magnitudo FFT dari fungsi windowfftLength = 1024;magFJendela2 = abs(fft(jendela2, fftLength));magFJendela1 = abs(fft(jendela1, fftLength)); %Ganti namaJendela dengan window function yang anda pakaisubplot(2,1,1);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Rectangular Window');subplot(2,1,2);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela2(1:ceil(fftLength/2))));ylabel('dB');xlabel('Normalized Frequency');legend('Hamming Window'); % Window visualization tool by MATLABwvtool(jendela1, jendela2);

maka akan muncul grafik sebagai berikut

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-60

-40

-20

0

20

40

dB

Rectangular Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-100

-50

0

50

dB

Normalized Frequency

Hamming Window

10 20 30 40 500

0.2

0.4

0.6

0.8

1

Samples

Am

plitu

deTime domain

0 0.2 0.4 0.6 0.8-80

-60

-40

-20

0

20

40

Normalized Frequency ( rad/sample)

Mag

nitu

de (

dB)

Frequency domain

Cari fungsi jendela selain yang diatas (minimal tiga fungsi window selain diatas). Plot masing-masing lalu bandingkan dengan fungsi filter : fir1, ellip, cheby1.

Kesimpulan apa yang bisa anda peroleh ?

Fungsi window lain

Bartlett dan Blackman

% Sampling frequency in HzFs = 16000; % contoh Bartlett and Blackman window, banyak fungsi window lainnyajendela1 = bartlett(51);jendela2 = blackman(51); % Magnitudo FFT dari fungsi windowfftLength = 1024;magFJendela2 = abs(fft(jendela2, fftLength));magFJendela1 = abs(fft(jendela1, fftLength)); %Ganti namaJendela dengan window function yang anda pakaisubplot(2,1,1);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Bartlett Window');subplot(2,1,2);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela2(1:ceil(fftLength/2))));ylabel('dB');xlabel('Normalized Frequency');legend('Blackman Window'); % Window visualization tool by MATLABwvtool(jendela1, jendela2);

Diperoleh hasil sebagai berikut

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-100

-50

0

50dB

Bartlett Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-150

-100

-50

0

50

dB


Blackman Window

10 20 30 40 500

0.2

0.4

0.6

0.8

1

Samples

Am

plitu

de

Time domain

0 0.2 0.4 0.6 0.8-150

-100

-50

0

50


Mag

nitu

de (

dB)

Frequency domain

Chebyshev dan Hann

% Sampling frequency in HzFs = 16000; % contoh Chebyshev and Hann window, banyak fungsi window lainnyajendela1 = chebwin(51);jendela2 = hann(51); % Magnitudo FFT dari fungsi windowfftLength = 1024;magFJendela2 = abs(fft(jendela2, fftLength));magFJendela1 = abs(fft(jendela1, fftLength)); %Ganti namaJendela dengan window function yang anda pakaisubplot(2,1,1);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Chebyshev Window');subplot(2,1,2);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela2(1:ceil(fftLength/2))));ylabel('dB');xlabel('Normalized Frequency');legend('Hann Window'); % Window visualization tool by MATLABwvtool(jendela1, jendela2);

diperoleh hasil berikut

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-150

-100

-50

0

50

dB

Chebyshev Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-150

-100

-50

0

50

dB


Hann Window

10 20 30 40 500

0.2

0.4

0.6

0.8

1

Samples

Am

plitu

deTime domain

0 0.2 0.4 0.6 0.8-150

-100

-50

0

50


Mag

nitu

de (

dB)

Frequency domain

Kaiser dan Taylor

% Sampling frequency in HzFs = 16000; % contoh Kaiser and Taylor window, banyak fungsi window lainnyajendela1 = kaiser(51);jendela2 = taylorwin(51); % Magnitudo FFT dari fungsi windowfftLength = 1024;magFJendela2 = abs(fft(jendela2, fftLength));magFJendela1 = abs(fft(jendela1, fftLength)); %Ganti namaJendela dengan window function yang anda pakaisubplot(2,1,1);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Kaiser Window');subplot(2,1,2);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela2(1:ceil(fftLength/2))));ylabel('dB');xlabel('Normalized Frequency');legend('Taylor Window'); % Window visualization tool by MATLABwvtool(jendela1, jendela2);

didapatkan hasil berikut

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-60

-40

-20

0

20

40

dB

Kaiser Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-60

-40

-20

0

20

40

dB


Taylor Window

10 20 30 40 500.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Samples

Am

plitu

de

Time domain

0 0.2 0.4 0.6 0.8-60

-40

-20

0

20

40


Mag

nitu

de (

dB)

Frequency domain

Triang dan Boxcar

% Sampling frequency in HzFs = 16000; % contoh Triang and Boxcar window, banyak fungsi window lainnyajendela1 = triang(51);jendela2 = rectwin(51); % Magnitudo FFT dari fungsi windowfftLength = 1024;magFJendela2 = abs(fft(jendela2, fftLength));magFJendela1 = abs(fft(jendela1, fftLength)); %Ganti namaJendela dengan window function yang anda pakaisubplot(2,1,1);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Triang Window');subplot(2,1,2);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela2(1:ceil(fftLength/2))));ylabel('dB');xlabel('Normalized Frequency');legend('Boxcar Window'); % Window visualization tool by MATLABwvtool(jendela1, jendela2);

didapatkan hasil berikut

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-150

-100

-50

0

50

dB

Triang Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-60

-40

-20

0

20

40

dB


Boxcar Window

10 20 30 40 500

0.2

0.4

0.6

0.8

1

Samples

Am

plitu

deTime domain

0 0.2 0.4 0.6 0.8-120

-100

-80

-60

-40

-20

0

20

40


Mag

nitu

de (

dB)

Frequency domain

Kesimpulan:

Setiap fungsi window punya karakteristik masing – masing. Secara garis besar, perbedaan masing – masing fungsi ini akan berpengaruh pada nilai dB dan frekuensi normal, sehingga hal itu dapat dilihat dampak pada amplitude dan waktu yang dihasilkan.

Perbandingan 3 fungsi window dengan fungsi filter

Fungsi window

% Sampling frequency in HzFs = 16000; jendela1 = hann(51);jendela2 = flattopwin(51);jendela3 = Chebwin(51);jendela4 = Gausswin(51);jendela5 = Tukeywin(51);jendela6 = Kaiser(51); % Magnitudo FFT darifungsi windowfftLength = 1024;magFJendela6 = abs(fft(jendela6, fftLength));magFJendela5 = abs(fft(jendela5, fftLength));magFJendela4 = abs(fft(jendela4, fftLength));magFJendela3 = abs(fft(jendela3, fftLength));magFJendela2 = abs(fft(jendela2, fftLength));magFJendela1 = abs(fft(jendela1, fftLength)); %GantinamaJendeladengan window function yang andapakaisubplot(6,1,1);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Hann Window');subplot(6,1,2);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Flattopwin Window');subplot(6,1,3);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Chebyshev Window');subplot(6,1,4);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Gausswin Window');subplot(6,1,5);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela1(1:ceil(fftLength/2))));ylabel('dB');legend('Tukeywin Window');subplot(6,1,6);plot(linspace(0,0.5,ceil(fftLength/2)), 20*log10(magFJendela2(1:ceil(fftLength/2))));ylabel('dB');xlabel('Normalized Frequency');legend('Kaiser Window'); % Window visualization tool by MATLABwvtool(jendela1, jendela2, jendela3, jendela4, jendela5, jendela6);

dan didapatkan hasil berikut

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-200

0200

dB

Hann Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-200

0200

dB

Flattopwin Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-200

0200

dB

Chebyshev Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-200

0200

dB

Gausswin Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-200

0200

dB

Tukeywin Window

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-200

0200

dB


Kaiser Window

10 20 30 40 50-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Samples

Am

plitu

de

Time domain

0 0.2 0.4 0.6 0.8-200

-150

-100

-50

0

50


Mag

nitu

de (

dB)

Frequency domain

Fungsi filter

LPF = fir1(50,[0.2 0.5]);freqz(LPF,0.5,1025)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-2000

-1000

0

1000


Pha

se (

degr

ees)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-100

-50

0

50


Mag

nitu

de (

dB)

[b,a] = ellip(20,3,30,200/500);freqz(b,a,1025,1000)title('n=20 Lowpass Elliptic Filter')

0 50 100 150 200 250 300 350 400 450 500-600

-400

-200

0

200

Frequency (Hz)

Pha

se (

degr

ees)

0 50 100 150 200 250 300 350 400 450 500-100

-50

0

Frequency (Hz)

Mag

nitu

de (

dB)

n=20 Lowpass Elliptic Filter

[b,a] = cheby1(20,3,200/450);freqz(b,a,1025,1000)

0 50 100 150 200 250 300 350 400 450 500-2000

-1500

-1000

-500

0

Frequency (Hz)

Pha

se (

degr

ees)

0 50 100 150 200 250 300 350 400 450 500-600

-400

-200

0

Frequency (Hz)

Mag

nitu

de (

dB)

Kesimpulan:

Setelah membandingkan antara window function dengan fungsi filter, maka kesimpulan yang dapat diambil adalah bahwa pada window function, dapat diperoleh grafik yang menunjukkan noise asli yang rapat dan konstan. Namun pada grafik filter, noise yang diberikan, mendapat filter dari masing – masing fungsi filter sehingga grafik yang dihasilkan tidak konstan.

Bagian mana yang dikehendaki dan bagian filter/window mana yang tidak dikehendaki, Mengapa ?

- Bagian yang dikehendaki oleh window adalah grafik yang rapat dan konstan.

- Bagian yang dikehendaki oleh filter adalah bagian yang renggang.

- Bagian yang tidak dikehendaki window dan filter yaitu bagian noise

II. Time-Frequency Analysis

Spectrogram adalah analisa frekuensi yang bergantung pada waktu. Spectrogram merupakan visualisasi dari kekuatan spektrum sinyal suara dengan menggunakan metode estimasi kekuatan spektrum periodogram.

Ketik command line seperti berikut

T = 0:0.001:2;

X = chirp(T,100,1,200,'q');

spectrogram(X,128,120,128,1E3);

title('Quadratic Chirp');

dan didapatkan hasil seperti ini

0 50 100 150 200 250 300 350 400 450 500

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Frequency (Hz)

Quadratic Chirp

Tim

e

Pengertian Narrowband dan Wideband Spectrogram

Narrowband merupakan jenis spectrogram yang memiliki bandwith 45-50 Hz dengan kekuatan yang berbeda beda sehingga dapat memilih masing-masing harmonic.

Wideband merupakan jenis spectrogram yang memiliki bandwith 300-500 Hz. Pada Wideband ini ketika digunakan untuk berbicara normal dengan frekuensi dasar sekitar 100-200 Hz, akan mengambil energi dari beberapa harmonic.

Modifikasi source code diatas agar mendapatkan kedua jenis spectrogram itu. Terkait dengan pertanyaan no.3, jelaskan mengapa narrowband dan wideband spectrogram tidak dikehendaki.

%Narrowband

t_window_narrowband = .005;

t_overlap_narrowband = .001;

T = 0:0.001:2;

Fs = 1000;

y = chirp(T,100,1,200,'q');

nfft_narrowband = 1024;

window_narrowband = t_window_narrowband * Fs;

noverlap_narrowband = t_overlap_narrowband * Fs;

jendela = window_narrowband;

noverlap = noverlap_narrowband;

subplot(2,1,1);

specgram(y,nfft_narrowband,Fs,jendela,noverlap);

xlabel('Time(sec)');

ylabel('Frekuensi (Hz)');

title('narrowband spectrogram');

%Wideband

t_window_wideband = .005;

t_overlap_wideband = .001;

window_wideband = t_window_wideband*Fs;

noverlap_wideband = 1;

nfft_wideband = 9600;

jendela = window_wideband;

noverlap = noverlap_wideband;

nfft = nfft_wideband;

subplot (2,1,2)

specgram(y,nfft_wideband,Fs,jendela,noverlap);

xlabel ('time(sec)');

ylabel ( 'Frekuensi (Hz)');

title('wideband spectrogram');

dan didapatkan hasil berikut

Time(sec)

Fre

kuen

si (

Hz)

narrowband spectrogram

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)

wideband spectrogram

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Ubah jenis window pada spectrogram, lihat soal no. I Fungsi window dan filter diatas. Urutkan window mana yang paling cocok, sertai dengan plot dan alasan mengapa.

Dengan Rectangular Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;

window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = rectwin (51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = rectwin (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');

dan didapat hasil berikut

Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Hamming window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = hamming (51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = hamming (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Bartlett Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = bartlett(51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = bartlett (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Blackman Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = blackman(51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = blackman (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Chebyshev Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = chebwin (51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = chebwin (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Hann Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = hann (51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = hann (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Kaiser Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = kaiser (51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = kaiser (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Taylor Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = taylorwin (51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = taylorwin (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Triang Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = triang (51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = triang (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Dengan Boxcar Window

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = chirp(T,100,1,200,'q');nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = rectwin (51);noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = rectwin (51);noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram');


Time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

time(sec)

Fre

kuen

si (

Hz)


0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

200

400

Penjelasan:

III. Speech Analysis

Ketik command line berikut

%Narrowbandt_window_narrowband = .005;t_overlap_narrowband = .001;T = 0:0.001:2;Fs = 1000;y = 'iconk2.wav';nfft_narrowband = 1024;window_narrowband = t_window_narrowband * Fs;noverlap_narrowband = t_overlap_narrowband * Fs;jendela = window_narrowband;noverlap = noverlap_narrowband;subplot(2,1,1);specgram(y,nfft_narrowband,Fs,jendela,noverlap);xlabel('Time(sec)');ylabel('Frekuensi (Hz)');title('narrowband spectrogram wav'); %Widebandt_window_wideband = .005;t_overlap_wideband = .001;window_wideband = t_window_wideband*Fs;noverlap_wideband = 1;nfft_wideband = 9600;jendela = window_wideband;noverlap = noverlap_wideband;nfft = nfft_wideband;subplot (2,1,2)specgram(y,nfft_wideband,Fs,jendela,noverlap);xlabel ('time(sec)');ylabel ( 'Frekuensi (Hz)');title('wideband spectrogram wav');

didapat hasil berikut

Time(sec)

Fre

kuen

si (

Hz)

narrowband spectrogram wav

1 2 3 4 5 6 7 8

x 10-3

0

200

400

time(sec)

Fre

kuen

si (

Hz)

wideband spectrogram wav

1 2 3 4 5 6 7 8

x 10-3

0

200

400

Analisis spectrogram (narrowband dan wideband)

Mengganti Fs = 8000


didapat hasilnya adalah

Time(sec)

Fre

kuen

si (

Hz)


-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.250

1000

2000

3000

4000

time(sec)

Fre

kuen

si (

Hz)


-1 -0.5 0 0.5 1 1.5 20

1000

2000

3000

4000

Mengganti Fs = 16000


dan didapat

Time(sec)

Fre

kuen

si (

Hz)


-0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 0.120

2000

4000

6000

8000

time(sec)

Fre

kuen

si (

Hz)


-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.20

2000

4000

6000

8000

Mengganti Fs =

Praktikum IV Pengukuran Sinyal

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