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I am very interested to know how the top right figure in :http://en.wikipedia.org/wiki/Spectrogram is generated (the script) and how to analyse it i.e what information does it convey?I would appreciate a simplified answer with minimum mathematical jargons. Thank you.

The plot shows time along the horizontal axis, and frequency along the vertical axis. With pixel color showing the intensity of each frequency at each time.

A spectrogram is generated by taking a signal and chopping it into small time segments, doing a Fourier series on each segment.

here is some matlab code to generate one.

Notice how plotting the signal directly, it looks like garbage, but plotting the spectrogram, we can clearly see the frequencies of the component signals.

%%%%%%%%
%% setup
%%%%%%%%

%signal length in seconds
signalLength = 60+10*randn();

%100Hz sampling rate
sampleRate = 100;
dt = 1/sampleRate;

%total number of samples, and all time tags
Nsamples = round(sampleRate*signalLength);
time = linspace(0,signalLength,Nsamples);

%%%%%%%%%%%%%%%%%%%%%
%create a test signal
%%%%%%%%%%%%%%%%%%%%%

%function for converting from time to frequency in this test signal
F1 = @(T)0+40*T/signalLength; #frequency increasing with time
M1 = @(T)1-T/signalLength;    #amplitude decreasing with time

F2 = @(T)20+10*sin(2*pi()*T/signalLength); #oscilating frequenct over time
M2 = @(T)1/2;                              #constant low amplitude

%Signal frequency as a function of time
signal1Frequency = F1(time);
signal1Mag = M1(time);

signal2Frequency = F2(time);
signal2Mag = M2(time);

%integrate frequency to get angle
signal1Angle = 2*pi()*dt*cumsum(signal1Frequency);
signal2Angle = 2*pi()*dt*cumsum(signal2Frequency);

%sin of the angle to get the signal value
signal = signal1Mag.*sin(signal1Angle+randn()) + signal2Mag.*sin(signal2Angle+randn());

figure();
plot(time,signal)


%%%%%%%%%%%%%%%%%%%%%%%
%processing starts here
%%%%%%%%%%%%%%%%%%%%%%%

frequencyResolution = 1
%time resolution, binWidth, is inversly proportional to frequency resolution
binWidth = 1/frequencyResolution;

%number of resulting samples per bin
binSize = sampleRate*binWidth;

%number of bins
Nbins = ceil(Nsamples/binSize);

%pad the data with zeros so that it fills Nbins
signal(Nbins*binSize+1)=0;
signal(end) = [];

%reshape the data to binSize by Nbins
signal = reshape(signal,[binSize,Nbins]);

%calculate the fourier transform
fourierResult = fft(signal);

%convert the cos+j*sin, encoded in the complex numbers into magnitude.^2
mags= fourierResult.*conj(fourierResult);

binTimes = linspace(0,signalLength,Nbins);
frequencies = (0:frequencyResolution:binSize*frequencyResolution);
frequencies = frequencies(1:end-1);

%the upper frequencies are just aliasing, you can ignore them in this example.
slice = frequencies<max(frequencies)/2;

%plot the spectrogram
figure();
pcolor(binTimes,frequencies(slice),mags(slice,:));

The inverse Fourier transform of the fourierResult matrix, will return the original signal.