Spectrograms

Spectrogram of "sarigamapa" audio

Flute

Gitar

Piano

Violin

Trumpt

Activity 6.1

Spectrogram

[x, Fs] = audioread('sarigamapa.wav');

x = x(:,1);

window_size = 2048;

hop_size = window_size/2;

[s,f,t] = spectrogram(x, window_size, hop_size,window_size,Fs,'yaxis');

waterplot(s,f,t)

figure()

subplot(121)

plot(x,'k')

xlabel('#samples')

ylabel('Amplitude')

grid on

subplot(122)

spectrogram(x, window_size, hop_size,window_size,Fs,'yaxis');

Change Window

hamming_window = window(@hamming,window_size);

hanning_window = window(@hanning,window_size);

kaiser_window = window(@kaiser,window_size);

gausswin_window = window(@gausswin,window_size);

Visualization

figure(1)

subplot(121)

plot(hamming_window,'LineWidth',2,'Color','k')

xlabel('#samples')

ylabel('Amplitude')

grid on

subplot(122)

spectrogram(x,hamming_window,hop_size,window_size,Fs,'yaxis');

sgtitle('Hamming Window')

figure(2)

subplot(121)

plot(hanning_window,'LineWidth',2,'Color','k')

xlabel('#samples')

ylabel('Amplitude')

grid on

subplot(122)

spectrogram(x,hanning_window,hop_size,window_size,Fs,'yaxis');

sgtitle('Hanning Window')

figure(3)

subplot(121)

plot(kaiser_window,'LineWidth',2,'Color','k')

xlabel('#samples')

ylabel('Amplitude')

grid on

subplot(122)

spectrogram(x,kaiser_window,hop_size,window_size,Fs,'yaxis');

sgtitle('Kaiser Window')

figure(4)

subplot(121)

plot(gausswin_window,'LineWidth',2,'Color','k')

xlabel('#samples')

ylabel('Amplitude')

grid on

subplot(122)

spectrogram(x,gausswin_window,hop_size,window_size,Fs,'yaxis');

sgtitle('Gausswin Window')

Waterplot Function

function waterplot(s,f,t)

% Waterfall plot of spectrogram

waterfall(f,t,abs(s)'.^2)

set(gca, 'XDir',"reverse", 'view',[30 50])

xlabel("Frequency (Hz)")

ylabel("Time (s)")

end

Activity 6.2

Compare Instruments Spectrograms

fs = 44100;

[y1,~] = audioread("andean-flute-theme-low_fybeqsnu1.mp3");

[y2,~] = audioread("zo_-pripyati-gitara.mp3");

[y3,~] = audioread("cirk-barabannaya-drob-i-marsh_-silachi.mp3");

[y4,~] = audioread("jamaican-piano_mytjcqbd.mp3");

[y5,~] = audioread("violin-trill_mk15cun_.mp3");

[y6,~] = audioread("z_uk-truba.mp3");

Create samples from audio files

n_start = 20000;

n_samples = 10000;

n_stop = n_start+n_samples;

flute = y1(n_start:n_stop,1);

gitar = y2(n_start:n_stop,1);

drum = y3(n_start:n_stop,1);

piano = y4(n_start:n_stop,1);

violin = y5(n_start:n_stop,1);

trumpt = y6(n_start:n_stop,1);

window_size = 2048;

hop_size = window_size/2;

Visualization

figure()

spectrogram(flute, window_size, hop_size,window_size,fs,'yaxis');

title('Flute Spectrogram')

figure()

plot(flute,'b','LineWidth',1)

xlim([0,n_samples])

xlabel('#samples')

ylabel('Amplitude')

title('Flute Waveform')

grid on

figure()

spectrogram(gitar, window_size, hop_size,window_size,fs,'yaxis');

title('Gitar Spectrogram')

figure()

plot(gitar,'k','LineWidth',1)

xlim([0,n_samples])

xlabel('#samples')

ylabel('Amplitude')

title('Gitar Waveform')

grid on

figure()

spectrogram(drum, window_size, hop_size,window_size,fs,'yaxis');

title('Drum Spectrogram')

figure()

plot(drum,'r','LineWidth',1)

xlim([0,n_samples])

xlabel('#samples')

ylabel('Amplitude')

title('Drum Waveform')

grid on

figure()

spectrogram(piano, window_size, hop_size,window_size,fs,'yaxis');

title('Piano Spectrogram')

figure()

plot(piano,'g','LineWidth',1)

xlim([0,n_samples])

xlabel('#samples')

ylabel('Amplitude')

title('Piano Waveform')

grid on

figure()

spectrogram(violin, window_size, hop_size,window_size,fs,'yaxis');

title('Violin Spectrogram')

figure()

plot(violin,'k','LineWidth',1)

xlim([0,n_samples])

xlabel('#samples')

ylabel('Amplitude')

title('Violin Waveform')

grid on

figure()

spectrogram(trumpt, window_size, hop_size,window_size,fs,'yaxis');

title('Trumpt Spectrogram')

figure()

plot(trumpt,'b','LineWidth',1)

xlim([0,n_samples])

xlabel('#samples')

ylabel('Amplitude')

title('Trumpt Waveform')

grid on

Spectrogram using Python

import numpy as np

import matplotlib.pyplot as plt

from scipy.io import wavfile

# read wav file

audio_file_path = r'sarigamapa.wav'

fs, data = wavfile.read(audio_file_path)

data = data[:,0]

# create window function

window_size = 256

window = np.hanning(window_size)

# create spectrogram

spectrogram = np.zeros((int(len(data)/window_size),window_size))

for i in range(spectrogram.shape[0]):

segment = data[i*window_size:(i+1)*window_size]

segment = segment*window

fft_data = np.fft.fft(segment)

fft_data = np.abs(fft_data)

spectrogram[i,:] = fft_data[:window_size]

# plot spectrogram

plt.imshow(spectrogram.T, aspect='auto')

plt.xlabel('Time')

plt.ylabel('Frequency')

plt.show()