Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel

Vassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate Jones, Mark Girolami
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:913-921, 2014.

Abstract

We study the problem of identifying bat species from echolocation calls in order to build automated bioacoustic monitoring algorithms. We employ the Dynamic Time Warping algorithm which has been successfully applied for bird flight calls identification and show that classification performance is superior to hand crafted call shape parameters used in previous research. This highlights that generic bioacoustic software with good classification rates can be constructed with little domain knowledge. We conduct a study with field data of 21 bat species from the north and central Mexico using a multinomial probit regression model with Gaussian process prior and a full EP approximation of the posterior of latent function values. Results indicate high classification accuracy across almost all classes while misclassification rate across families of species is low highlighting the common evolutionary path of echolocation in bats.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-stathopoulos14, title = {{Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel}}, author = {Stathopoulos, Vassilios and Zamora-Gutierrez, Veronica and Jones, Kate and Girolami, Mark}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {913--921}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/stathopoulos14.pdf}, url = {https://proceedings.mlr.press/v33/stathopoulos14.html}, abstract = {We study the problem of identifying bat species from echolocation calls in order to build automated bioacoustic monitoring algorithms. We employ the Dynamic Time Warping algorithm which has been successfully applied for bird flight calls identification and show that classification performance is superior to hand crafted call shape parameters used in previous research. This highlights that generic bioacoustic software with good classification rates can be constructed with little domain knowledge. We conduct a study with field data of 21 bat species from the north and central Mexico using a multinomial probit regression model with Gaussian process prior and a full EP approximation of the posterior of latent function values. Results indicate high classification accuracy across almost all classes while misclassification rate across families of species is low highlighting the common evolutionary path of echolocation in bats.} }
Endnote
%0 Conference Paper %T Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel %A Vassilios Stathopoulos %A Veronica Zamora-Gutierrez %A Kate Jones %A Mark Girolami %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-stathopoulos14 %I PMLR %P 913--921 %U https://proceedings.mlr.press/v33/stathopoulos14.html %V 33 %X We study the problem of identifying bat species from echolocation calls in order to build automated bioacoustic monitoring algorithms. We employ the Dynamic Time Warping algorithm which has been successfully applied for bird flight calls identification and show that classification performance is superior to hand crafted call shape parameters used in previous research. This highlights that generic bioacoustic software with good classification rates can be constructed with little domain knowledge. We conduct a study with field data of 21 bat species from the north and central Mexico using a multinomial probit regression model with Gaussian process prior and a full EP approximation of the posterior of latent function values. Results indicate high classification accuracy across almost all classes while misclassification rate across families of species is low highlighting the common evolutionary path of echolocation in bats.
RIS
TY - CPAPER TI - Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel AU - Vassilios Stathopoulos AU - Veronica Zamora-Gutierrez AU - Kate Jones AU - Mark Girolami BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-stathopoulos14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 913 EP - 921 L1 - http://proceedings.mlr.press/v33/stathopoulos14.pdf UR - https://proceedings.mlr.press/v33/stathopoulos14.html AB - We study the problem of identifying bat species from echolocation calls in order to build automated bioacoustic monitoring algorithms. We employ the Dynamic Time Warping algorithm which has been successfully applied for bird flight calls identification and show that classification performance is superior to hand crafted call shape parameters used in previous research. This highlights that generic bioacoustic software with good classification rates can be constructed with little domain knowledge. We conduct a study with field data of 21 bat species from the north and central Mexico using a multinomial probit regression model with Gaussian process prior and a full EP approximation of the posterior of latent function values. Results indicate high classification accuracy across almost all classes while misclassification rate across families of species is low highlighting the common evolutionary path of echolocation in bats. ER -
APA
Stathopoulos, V., Zamora-Gutierrez, V., Jones, K. & Girolami, M.. (2014). Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:913-921 Available from https://proceedings.mlr.press/v33/stathopoulos14.html.

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