Name
Title | ||
|---|---|---|
A robust automatic bird phrase classifier using dynamic time-warping with prominent region identification |
Abstract | ||
|---|---|---|
In this paper, we present a novel approach to birdsong phase classification using template-based techniques suitable even for limited training data and noisy environments. The algorithm utilizes dynamic time-warping and prominent (high-energy) time-frequency regions of training spectrograms to derive templates. The algorithm is evaluated on 32 classes of Cassin's Vireo bird phrases. Using only three training examples per class, our algorithm yields a phrase accuracy of 96.23%, outperforming other classifiers (e.g. 85.21% classification accuracy of SVM). In the presence of additive noise (10 dB SNR degradation), the proposed classifier does not degrade significantly, compared to others. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6637752 | ICASSP |
Keywords | Field | DocType |
noisy environments,robust automatic bird phrase classifier,training spectrograms,noise-robust,prominent time-frequency regions,template-based,spectral analysis,bird phrase classification,limited data,signal classification,template-based techniques,dynamic time-warping,phrase accuracy,cassin vireo bird phrases,birdsong phase classification,training data,region identification,time-frequency analysis,additive noise,vectors,dynamic time warping,time frequency analysis,speech,databases,robustness | Training set,Pattern recognition,Dynamic time warping,Vireo,Spectrogram,Computer science,Support vector machine,Phrase,Speech recognition,Artificial intelligence,Time–frequency analysis,Classifier (linguistics) | Conference |
ISSN | Citations | PageRank |
1520-6149 | 5 | 0.55 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kantapon Kaewtip | 1 | 8 | 1.27 |
| Lee Ngee Tan | 2 | 43 | 5.00 |
| Abeer Alwan | 3 | 729 | 88.19 |
| Charles E. Taylor | 4 | 178 | 30.99 |