Hi Sravantej, You have to understand that there are constraints on how precise you can obtain a time-frequency analysis of a signal.
Having said that, you can use spectrogram in the Signal Processing Toolbox and/or cwtft in the Wavelet Toolbox.
Here is an example with spectrogram:
t=0:0.001:2;
x=chirp(t,0,1,150);
[y,f,t,p] = spectrogram(x,256,250,1000);
surf(t,f,10*log10(abs(p)),'EdgeColor','none');
axis xy; axis tight; colormap(jet); view(0,90);
xlabel('Time');
ylabel('Frequency (Hz)');
Here is an example with cwtft from the Wavelet Toolbox:
N = 1024;
t = linspace(0,1,N);
dt =1/(N-1);
Y = sin(8*pi*t).*(t<=0.5) + sin(16*pi*t).*(t>0.5);
s0 = 6*dt; ds = 0.15; NbSc = 50;
wname = 'morl';
SCA = {s0,ds,NbSc};
cwtsig = cwtft({Y,dt},'scales',SCA,'wavelet',wname);
MorletFourierFactor = 4*pi/(6+sqrt(2+6^2));
Scales = cwtsig.scales.*MorletFourierFactor;
Freq = 1./Scales;
imagesc(t,[],abs(cwtsig.cfs));
indices = get(gca,'ytick');
set(gca,'yticklabel',Freq(indices));
xlabel('Time'); ylabel('Hz');
title('Time-Frequency Analysis with CWT');
