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Classification of bird song syllables using Wigner-Ville ambiguity function cross-terms

A novel feature extraction method for low-dimensional signal representation is presented. The features are useful for classification of non-stationary multi-component signals with stochastic variation in amplitudes and time-frequency locations. Using a penalty function to suppress the Wigner-Ville ambiguity function auto-terms, the proposed feature set is based on the cross-term doppler- and lag profiles. The investigation considers classification where strong similar components appear in all signals and where the differences between classes are related to weaker components. The approach is evaluated and compared with established methods for simulated data and bird song syllables of the great reed warbler. The results show that the novel feature extraction method gives a better classification than established methods used in bird song analysis.