Praktikum IV Pengukuran Sinyal

transcript

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

Rectangular Window

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

Normalized Frequency

Hamming Window

10 20 30 40 500

Samples

deTime domain

0 0.2 0.4 0.6 0.8-80

Normalized Frequency ( rad/sample)

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

Bartlett Window

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

Blackman Window

10 20 30 40 500

Samples

Time domain

0 0.2 0.4 0.6 0.8-150

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

Chebyshev Window

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

Hann Window

10 20 30 40 500

Samples

deTime domain

0 0.2 0.4 0.6 0.8-150

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

Kaiser Window

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

Taylor Window

10 20 30 40 500.2

Samples

Time domain

0 0.2 0.4 0.6 0.8-60

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

Triang Window

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

Boxcar Window

10 20 30 40 500

Samples

deTime domain

0 0.2 0.4 0.6 0.8-120

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

Hann Window

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

Flattopwin Window

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

Chebyshev Window

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

Gausswin Window

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

Tukeywin Window

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

Kaiser Window

10 20 30 40 50-0.2

Samples

Time domain

0 0.2 0.4 0.6 0.8-200

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

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

[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

Frequency (Hz)

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

Frequency (Hz)

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

Frequency (Hz)

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

Frequency (Hz)

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

Frequency (Hz)

Quadratic Chirp

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)

narrowband spectrogram

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

wideband spectrogram

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

time(sec)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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)

narrowband spectrogram wav

1 2 3 4 5 6 7 8

x 10-3

time(sec)

wideband spectrogram wav

1 2 3 4 5 6 7 8

x 10-3

Analisis spectrogram (narrowband dan wideband)

Mengganti Fs = 8000

didapat hasilnya adalah

Time(sec)

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.250

time(sec)

-1 -0.5 0 0.5 1 1.5 20

Mengganti Fs = 16000

dan didapat

Time(sec)

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

time(sec)

-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.20

Mengganti Fs =

Praktikum IV Pengukuran Sinyal

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