@InProceedings{8081507,
  author = {M. Sandsten and J. Brynolfsson},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Classification of bird song syllables using Wigner-Ville ambiguity function cross-terms},
  year = {2017},
  pages = {1739-1743},
  abstract = {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.},
  keywords = {feature extraction;signal classification;signal representation;stochastic processes;penalty function;Wigner-Ville ambiguity function auto-terms;cross-term doppler;lag profiles;bird song syllables;bird song analysis;low-dimensional signal representation;feature extraction method;Wigner-Ville ambiguity function cross-terms;nonstationary multicomponent signal classification;great reed warbler;stochastic variation;Time-frequency analysis;Mel frequency cepstral coefficient;Spectrogram;Birds;Signal to noise ratio;Jitter;Kernel},
  doi = {10.23919/EUSIPCO.2017.8081507},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570342748.pdf},
}

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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. 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Brynolfsson},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Classification of bird song syllables using Wigner-Ville ambiguity function cross-terms},\n year = {2017},\n pages = {1739-1743},\n abstract = {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. 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