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      warbleR: anrpackage to streamline analysis of animal acoustic signals

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      Methods in Ecology and Evolution
      Wiley-Blackwell

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          Song as an aggressive signal in songbirds

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            Quantitative Analysis of Animal Vocal Phonology: an Application to Swamp Sparrow Song

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              Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study.

              The performance of two techniques is compared for automated recognition of bird song units from continuous recordings. The advantages and limitations of dynamic time warping (DTW) and hidden Markov models (HMMs) are evaluated on a large database of male songs of zebra finches (Taeniopygia guttata) and indigo buntings (Passerina cyanea), which have different types of vocalizations and have been recorded under different laboratory conditions. Depending on the quality of recordings and complexity of song, the DTW-based technique gives excellent to satisfactory performance. Under challenging conditions such as noisy recordings or presence of confusing short-duration calls, good performance of the DTW-based technique requires careful selection of templates that may demand expert knowledge. Because HMMs are trained, equivalent or even better performance of HMMs can be achieved based only on segmentation and labeling of constituent vocalizations, albeit with many more training examples than DTW templates. One weakness in HMM performance is the misclassification of short-duration vocalizations or song units with more variable structure (e.g., some calls, and syllables of plastic songs). To address these and other limitations, new approaches for analyzing bird vocalizations are discussed.
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                Author and article information

                Journal
                Methods in Ecology and Evolution
                Methods Ecol Evol
                Wiley-Blackwell
                2041210X
                February 2017
                February 08 2017
                : 8
                : 2
                : 184-191
                Article
                10.1111/2041-210X.12624
                920462b8-e7fa-4ad6-a1a4-fea85b0f2828
                © 2017

                http://doi.wiley.com/10.1002/tdm_license_1

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