![]() Appl Soft Comput 81:105501Ĭakır E, Parascandolo G, Heittola T, Huttunen H, Virtanen T (2017) Convolutional recurrent neural networks for polyphonic sound event detection. J Acoust Soc Am 131(6):4640–4650īrown A, Garg S, Montgomery J (2019) Automatic rain and cicada chorus filtering of bird acoustic data. Methods Ecol Evol 10(10):1796–1807īriggs F, Lakshminarayanan B, Neal L, Fern XZ, Raich R, Hadley SJK, Hadley AS, Betts MG (2012) Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach. Cambridge University Press, Cambridgeīradfer-Lawrence T, Gardner N, Bunnefeld L, Bunnefeld N, Willis SG, Dent DH (2019) Guidelines for the use of acoustic indices in environmental research. In: Saad D (ed) Online learning and neural networks. Ecol Appl 17(8):2137–2144īottou L (1998) Online algorithms and stochastic approximations. ![]() IEEE, pp 737–742īoelman NT, Asner GP, Hart PJ, Martin RE (2007) Multi-trophic invasion resistance in hawaii: bioacoustics, field surveys, and airborne remote sensing. In: IEEE international conference on data mining. Quantification via probability estimators. arXiv preprint arXiv:1510.04811īella A, Ferri C, Hernández-Orallo J, Ramirez-Quintana MJ (2010). Ecol Indic 75:95–100īeijbom O, Hoffman J, Yao E, Darrell T, Rodriguez-Ramirez A, Gonzalez-Rivero M, Guldberg OH (2015) Quantification in-the-wild: data-sets and baselines. īedoya C, Isaza C, Daza JM, López JD (2017) Automatic identification of rainfall in acoustic recordings. Moreover, we show that a more compact network can outperform a deeper one for fine-grained scenarios of birds and anurans species.Īalborg University (2004) The mel frequency scale and coefficients. Results indicate quantification has advantages over both acoustic features alone and the use of regular classification networks, in particular in terms of generalization and class recall making it a suitable choice for segregation tasks related to soundscape ecology. This paper investigates the use of quantification combined with classification loss in order to train a convolutional neural network to classify species of birds and anurans. In the context of counting the number of classes in observations, the quantification task is attracting attention and was shown to be effective in other applications. Deep neural networks have become state-of-the-art for feature learning in many multi-class applications, but they often present issues such as over-fitting or achieve unbalanced performances for different classes, which can hamper the deployment of such models in realistic scenarios. ![]() However, in many cases, the problem is difficult due to high-class overlap in terms of time-frequency characteristics, as well as the presence of noise. ![]() In soundscape ecology analysis, the use of acoustic features is well established and offers important baselines to ecological analyses. ![]()
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