| Paper: | MLSP-P7.4 | ||
| Session: | Pattern Recognition and Classification II | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | BIRD SONG RECOGNITION BASED ON SYLLABLE PAIR HISTOGRAMS | ||
| Authors: | Panu Somervuo; Helsinki University of Technology | ||
| Aki Härmä; Helsinki University of Technology | |||
| Abstract: | Bird song can be divided into a sequence of syllabic elements. In this paper we investigate the possibility of bird species recognition based on the syllable pair histogram of the song. This representation compresses the variable-length syllable sequence into a fixed-dimensional feature vector. The histogram is computed by means of Gaussian syllable prototypes which are automatically found given the song data and the dissimilarity measure of syllables. Our representation captures the use of the syllable alphabet and also some temporal structure of the song. We demonstrate the method in bird species recognition with song patterns obtained from fifty individuals belonging to four common passerine bird species. | ||
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