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copied!<p>What you need is the Time-Frequency localization information. This can be obtained using <a href="http://en.wikipedia.org/wiki/Short-time_Fourier_transform" rel="nofollow">Short-time Fourier transform</a>. There are many other Time-Frequency analysis techniques, STFT being the simplest and hence a good starting point. Here is a simple code to help you understand the concept:</p> <pre><code>% Parameters Fs = 44100; % Sample rate (44100 Hz) t = 0:1/Fs:5; % Time instances t1 = 5; % End time of signal, 5 secs f0 = 440; % frequency swiped from 440 Hz f1 = 880; % to 880 Hz % Signal generation audio = chirp(t,f0,t1,f1); % wavplay(audio,Fs) % to play the audio % Signal analysis window = 2050; % Should be minimum twice the maximum frequency we want to analyze noverlap = 1025; % 50% overlap nfft = 44100; [S,F,T,P] = spectrogram(audio,window,noverlap,nfft,Fs); % Spectrogram takes the STFT of the signal % P matrix contains the power spectral density of each segment of the STFT % Plotting results imagesc(real(S)) % frequency-time Plot of the signal ylim([0 1000]) xlabel('Time (secs)') ylabel('Frequency (Hz)') title('Time-Frequency plot of a Audio signal') </code></pre> <p>To get the sample number, you just need to find the time instance at which the frequency of your interest appears, and use sampling frequency to compute the sample number.</p> <p>P is the power spectral density matrix. Along y-axis is the frequency, x-axis is time, and power contributed by each frequency at every instant is stored in this matrix. You need the element which has the highest value in the entire matrix. A code like below should work:</p> <pre><code>[maxElement, maxElementTimeIndex] = max(max(P, [], 1)); % max(P, [], 1) finds maximum power for each time instance, max(max(P,[],1)) finds maximum for the entire 2D matrix. maxPoweredSampleInAudioSignal = (maxElementTimeIndex-1) * Fs; % This calculation is made within the limitations of STFT, so approximately here the maximum power for any frequency is present </code></pre>

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