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Kindly explain me abt this command

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moonman
moonman 2011 年 10 月 29 日
Can anybody explain me this command
ss = (mean(conv(tones(:,1),hh).^2) > mean(tones(:,1).^2)/5);
This command the book is using for DTMF decoding Plz explain me this

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moonman
moonman 2011 年 10 月 29 日
Yes you are exactly right. This portion is related to detection for presence of specific tone. Thanks for u reply I just want to ask now why book has scaled it by factor of 1/5
If i make it 1/20 or by 1/2 what will be the impact.
I have one more query. In equation
h[n] = (2/L) cos ( 2 π fb n / fs ), 0≤ n < L
where L is filter length, fb center frequency of the filter (frequency to be filtered), n is number of filter points, and fs is sampling frequency on which filter is implemented.
What we mean by Filter Points. Can u kindly explain
  2 件のコメント
moonman
moonman 2011 年 10 月 29 日
What is the difference between L and n
moonman
moonman 2011 年 10 月 29 日
Is the length of Filter and Number of Tabs in same thing

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その他の回答 (2 件)

Wayne King
Wayne King 2011 年 10 月 29 日
Hi, This is comparing the mean value of the squared values of a convolution with the mean of the squared values of the waveform scaled by 1/5.
I'm assuming this is part of the detection algorithm to decide which tone is present.
Filtering the waveform (convolving) with a frequency-selective filter should remove a lot of the frequency content. If most of the signal's energy is in the passband of the filter, you will still be left with a sizable proportion of the total energy, if not, then you will not.
ss is a 1 or 0 which tests that hypothesis. It's 1 if most of the energy is in the passband of the filter, and 0 if it is not.

Wayne King
Wayne King 2011 年 10 月 29 日
I'm not sure exactly why they picked 5 here. Perhaps it's an empirical finding?
If you increased it to say 20, then that will reduce the value of
mean(tones(:,1).^2)/N);
where N is the specific value, that would mean that ss would be 1 in more cases. In other words, that would result in many more decisions that the tone was present and perhaps too many false positives.
On the other hand, making it 1/2 would have the opposite effect.

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