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Matched filters do an excellent job of identifying sound samples, so we decided to apply the method here to identify birdcall audiofiles. A matched filter searches for a sample clip, the filter, within a longer audio recording. Convolution compares the filter to the longer signal at eachpossible offset. The greater the maximum amplitude of the convolution result, the stronger the match. By having a different filter for each birdcall, we cansearch an audio file to identify which birdcall it contains.
The matched filter algorithm is as follows:
Our first step in implementing the matched filter
algorithm was to create a library of birdcall filters. To do this, we looked atthe spectrograms of a few sample audio files of the same birdcall and selected a
portion that looked representative of the call.
For each birdcall, one of the representative audio segments was saved as a filter. Because the first two steps of the above matchedfilter algorithm affect only the filter library and are independent of the input signal, we reversed the filters and normalized their energy before saving themto wave files.
The following MATLAB script performed our matched filter algorithm. When given a wave file as input, it would tell us how well theaudio sample matched against each of the 6 birdcall filters.
function result = birdcheck(file)
[sig, fs, nbits]= wavread(file);
signal=sig(:,1);signal=signal/max(abs(signal));
filters{1}=wavread('filters/bob.wav');filters{2}=wavread('filters/lt.wav');
filters{3}=wavread('filters/lw.wav');filters{4}=wavread('filters/pygmy.wav');
filters{5}=wavread('filters/red.wav');filters{6}=wavread('filters/redcry.wav');
for i=1:6filter=filters{i};
result(i) = max(abs(cconv(signal,filter(end:-1:1))));end
end
The script was able to correctly identify several birdcalls. It did fail to correctly identify four cases in twocategories:
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