The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Bird species recognition using support vector machines
EURASIP Journal on Applied Signal Processing
On the Studies of Syllable Segmentation and Improving MFCCs for Automatic Birdsong Recognition
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
| Hi-index | 0.00 |
Pattern recognition and digital signal processing techniques allow the design of automated systems for avian monitoring. They are a non-intrusive and cost-effective way to perform surveys of bird populations and assessments of biological diversity. In this study, a number of representation approaches for bird sounds are compared; namely, feature and dissimilarity representations. In order to take into account the non-stationary nature of the audio signals and to build robust dissimilarity representations, the application of the Earth Mover's Distance (EMD) to time-varying measurements is proposed. Measures of the leave-one-out 1-NN performance are used as comparison criteria. Results show that, overall, the Mel-ceptrum coefficients are the best alternative; specially when computed by frames and used in combination with EMD to generate dissimilarity representations.