428 research outputs found
Acoustic classification of Australian frogs for ecosystem survey
Novel bioacoustics signal processing techniques have been developed to classify frog vocalisations in both trophy and field recordings. The research is useful in helping ecologists monitor frog community activity and species richness over long-term. Two major contributions are the construction of novel feature descriptors in the Cepstral domain, and the design of novel classification systems for multiple simultaneously vocalising frog species
Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking
Barking is perhaps the most characteristic form
of vocalization in dogs; however, very little is known about
its role in the intraspecific communication of this species.
Besides the obvious need for ethological research, both in
the field and in the laboratory, the possible information
content of barks can also be explored by computerized
acoustic analyses. This study compares four different
supervised learning methods (naive Bayes, classification
trees, k-nearest neighbors and logistic regression) combined
with three strategies for selecting variables (all
variables, filter and wrapper feature subset selections) to
classify Mudi dogs by sex, age, context and individual
from their barks. The classification accuracy of the models
obtained was estimated by means of K-fold cross-validation.
Percentages of correct classifications were 85.13 %
for determining sex, 80.25 % for predicting age (recodified
as young, adult and old), 55.50 % for classifying contexts
(seven situations) and 67.63 % for recognizing individuals
(8 dogs), so the results are encouraging. The best-performing
method was k-nearest neighbors following a
wrapper feature selection approach. The results for classifying
contexts and recognizing individual dogs were better
with this method than they were for other approaches
reported in the specialized literature. This is the first time
that the sex and age of domestic dogs have been predicted
with the help of sound analysis. This study shows that dog
barks carry ample information regarding the caller’s
indexical features. Our computerized analysis provides
indirect proof that barks may serve as an important source
of information for dogs as well
Quantifying social-ecological scale mismatches suggests people should be managed at broader scales than ecosystems
Mapping permits and ecological data across the Great Barrier Reef Marine Park allowed us to quantify and rigorously compare interacting social and ecological scales. Institutions (permits) and ecological systems both varied at multiple scales. The scales of permissions were typically bimodal and larger than ecological scales. Thus, we propose that effective management may have to occur at broader scales than ecological variation. Further comparable examples are needed for establishing the generality of this proposition
A Methodology Based on Bioacoustic Information for Automatic Identification of Reptiles and Anurans
Nowadays, human activity is considered one of the main risk factors for the life of reptiles and amphibians. The presence of these living beings represents a good biological indicator of an excellent environmental quality. Because of their behavior and size, most of these species are complicated to recognize in their living environment with image devices. Nevertheless, the use of bioacoustic information to identify animal species is an efficient way to sample populations and control the conservation of these living beings in large and remote areas where environmental conditions and visibility are limited. In this chapter, a novel methodology for the identification of different reptile and anuran species based on the fusion of Mel and Linear Frequency Cepstral Coefficients, MFCC and LFCC, is presented. The proposed methodology has been validated using public databases, and experimental results yielded an accuracy above 95% showing the efficiency of the proposal
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