An automatic acoustic bat identification system based on the audible spectrum

Henríquez, Aarón; Alonso, Jesús B.; Travieso, Carlos M.; Rodríguez‐Herrera, Bernal; Bolaños, Federico; Alpízar, Priscilla; Ipiña, Karmele López de; Henríquez, Patricia

doi:10.1016/j.eswa.2014.02.021

Cited by 21 publications

(9 citation statements)

References 24 publications

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“…Classification accuracy ranged between 82% and 100% for the different species, depending on the classifier used. Henríquez et al (2014) studied a semi-automatic system for bat classification. For that purpose they built multiple GMM models for each species and then classified some manually selected audio files, each containing sound emissions of a single bat species.…”

Section: Introductionmentioning

confidence: 99%

“…A comparison of various machine learning approaches for the classification of bird and amphibian calls was provided by Acevedo, Corrada-Bravo, Corrada-Bravo, Villanueva-Rivera, and Aide (2009) and Henríquez et al (2014); while Stowell and Plumbley (2010) presented a comprehensive review of methods.…”

Section: Introductionmentioning

confidence: 99%

“…Recent technological developments, related to automated information retrieval from audio, have focused on the elaboration of methods in support of biological studies on the presence/absence and activity patterns of rare, threatened, or elusive species (Bardeli et al, 2010;Frommolt & Tauchert, 2014;Henríquez et al, 2014;Huang, Yang, Yang, & Chen, 2009;Potamitis, Ntalampiras, Jahn, & Riede, 2014) and tools in support of the non-intrusive and cost-effective assessment of biodiversity (Aide et al, 2013;Briggs et al, 2012;Digby, Towsey, Bell, & Teal, 2013;Stowell & Plumbley, 2014;Sueur, Pavoine, Hamerlynck, & Duvail, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Automated acoustic detection of Vanellus chilensis lampronotus

Ganchev

Jahn

Marques

et al. 2015

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated acoustic detection of Vanellus chilensis lampronotus

Ganchev

Jahn

Marques

et al. 2015

Expert Systems with Applications

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“…Exhaustive studies have been conducted in birds [12], amphibians [13], insects [14] and bats [15] with varying degrees of success. As for marine fauna, some works can be found in the literature concerning the acoustic identification of whales and dolphins.…”

Section: Introductionmentioning

confidence: 99%

Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals

2016

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Fish as well as birds, mammals, insects and other animals are capable of emitting sounds for diverse purposes, which can be recorded through microphone sensors. Although fish vocalizations have been known for a long time, they have been poorly studied and applied in their taxonomic classification. This work presents a novel approach for automatic remote acoustic identification of fish through their acoustic signals by applying pattern recognition techniques. The sound signals are preprocessed and automatically segmented to extract each call from the background noise. Then, the calls are parameterized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC), Shannon Entropy (SE) and Syllable Length (SL), yielding useful information for the classification phase. In our experiments, 102 different fish species have been successfully identified with three widely used machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 95.24%, 93.56% and 95.58%, respectively.

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“…However, the sounds were taken from noise-free sections of the recorded files. More recently, Henríquez et al [16] recognized seven different species of bats by Gaussian Mixture Models (GMM), achieving a low average error of 1.8%, using a combination of linear and non-linear parameters. There is no doubt of the progress made in the field of bioacoustic identification to enable an efficient classification of species.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Linear and Mel Frequency Cepstral Coefﬁcients for Automatic Classiﬁcation of Reptiles

2017

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Bioacoustic research of reptile calls and vocalizations has been limited due to the general consideration that they are voiceless. However, several species of geckos, turtles, and crocodiles are able to produce simple and even complex vocalizations which are species-specific. This work presents a novel approach for the automatic taxonomic identification of reptiles through their bioacoustics by applying pattern recognition techniques. The sound signals are automatically segmented, extracting each call from the background noise. Then, their calls are parametrized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC) to serve as features in the classification stage. In this study, 27 reptile species have been successfully identified using two machine learning algorithms: K-Nearest Neighbors (kNN) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 97.78% and 98.51%, respectively.

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An automatic acoustic bat identification system based on the audible spectrum

Cited by 21 publications

References 24 publications

Automated acoustic detection of Vanellus chilensis lampronotus

Automated acoustic detection of Vanellus chilensis lampronotus

Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals

Fusion of Linear and Mel Frequency Cepstral Coefﬁcients for Automatic Classiﬁcation of Reptiles

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