=Paper=
{{Paper
|id=Vol-1180/CLEF2014wn-Life-Northcott2014
|storemode=property
|title=Participation of Group SCS to LifeCLEF Bird Identification Challenge 2014
|pdfUrl=https://ceur-ws.org/Vol-1180/CLEF2014wn-Life-Northcott2014.pdf
|volume=Vol-1180
|dblpUrl=https://dblp.org/rec/conf/clef/Northcott14
}}
==Participation of Group SCS to LifeCLEF Bird Identification Challenge 2014==
<pdf width="1500px">https://ceur-ws.org/Vol-1180/CLEF2014wn-Life-Northcott2014.pdf</pdf>
<pre>
         Participation of group SCS to LifeCLEF bird
                 identification challenge 2014


                                  James Northcott


    Sales.info@wildflowersuk.com



1      Tasks performed
    Using the automatic call detection system based on the spectrogram correlation
method within Ishmael v2.3 bi-acoustic analysis freeware [2]. Manually generated
synthetic kernel [3] was created for a total of 14 audio test records and each kernel
was then cross-correlated with spectrograms from the full set of 9688 audio training
files. Only top 501 predictions included as per max requested. Probability not
calculated by system so figure shown was an arbitrary detection function.



2      Main objectives of experiment


   To establish how well the spectrogram correlation automatic detection within
Ishmael v2.3 [1] would perform on various birdcalls.



3      Approach used

   Each test audio record requiring approx 12 hours to process. Therefore time and
processing constraints limited to processing of only 14 test audio records.
   The system for determining probability rank compares test audio record with all
sounds within every record of the training data. Therefore initial results include
multiple duplicates of the same species. In order to meet with the task requirement of
501 maximum predictions, duplicate species had to be removed, to leave only the
highest prediction for each species.




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4      Resources used



    Ishmael v2.3   http://www.bioacoustics.us/ishmael.html [1]



5      Results obtained


  An overall score of zero was obtained (see graph below) which was last when
compared to the other 29 submitted runs.




6      Analysis of the results


   As mentioned in 3 above the time and processing constraints with this method
limited the analysis to just 14 of the 4339 test audio records.
   With these limitations, I suspect it would not have been possible to achieve an
overall score above zero. As yet it has not been possible to make any assessment of
where my predictions scored for the 14 audio test records that were analyzed.




                                       671
7      Perspectives for future work

   By participating in the LifeCLEF 2014 Bird Task, I was hoping to the demonstrate
that spectrogram correlation can be very useful for the automatic detection of certain
bird calls. At the same time, I think my participation also demonstrates the limitations
of freely available software currently available. Hopefully this may lead to
improvements of existing software or the development of new software. I would
consider the most useful improvements to be as follows:


• Improved detection algorithms and accuracy of automatic detection

• Improved method for quick and easy creation of synthetic kernels

• Possible combining of Spectrogram correlation with other detection methods such
  as energy summation to provide a more robust and accurate detection system

• More powerful and speedier processing capability.




                                   Bibliography



 1. David K. Mellinger, Ishmael 2.3, http://www.bioacoustics.us/ishmael.html

 2. David K. Mellinger, Ishmael 1.0 User’s guide : automatic detection

 3. Mellinger and Clarke, 2000, construction of kernels, and their performance.




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</pre>