Feature and Dissimilarity Representations for the Sound-Based Recognition of Bird Species
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.
KeywordsAutomated avian monitoring bird sounds dissimilarity representations feature representations
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