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

Automated robust Anuran classification by extracting elliptical feature pairs from audio spectrograms

This is the autjor accepted manuscript. The final version is available from IEEE via the DOI in this recordEcologists can assess the health of wetlands by monitoring populations of animals such as Anurans (i.e., frogs and toads), which are sensitive to habitat changes. But, surveying anurans requires trained experts to identify species from the animals' mating calls. This identification task can be streamlined by automation. To this end, we propose an automatic frog-call classification algorithm and a smartphone application that drastically simplify the monitoring of anuran populations. We offer three main contributions. First, we introduce a classification method that has an average accuracy of 86% on a dataset of 736 calls from 48 anuran species from the United States. Our dataset is much larger and diverse than those of previous works on anuran classification. Second, we extract a new type of spectrogram feature that avoids syllable segmentation and the manual cleaning of the recordings. Our method also works with recordings of variable length. Third, our method uses GPS location and a voting scheme to reliably deal with a large number of species and high levels of noise.National Science Foundatio

Improving classification algorithms by considering score series in wireless acoustic sensor networks

The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames,while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application

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

Automated call detection for acoustic surveys with structured calls of varying length

Funding: Y.W. is partly funded by the China Scholarship Council (CSC) for Ph.D. study at the University of St Andrews, UK.1. When recorders are used to survey acoustically conspicuous species, identification calls of the target species in recordings is essential for estimating density and abundance. We investigate how well deep neural networks identify vocalisations consisting of phrases of varying lengths, each containing a variable number of syllables. We use recordings of Hainan gibbon (Nomascus hainanus) vocalisations to develop and test the methods. 2. We propose two methods for exploiting the two-level structure of such data. The first combines convolutional neural network (CNN) models with a hidden Markov model (HMM) and the second uses a convolutional recurrent neural network (CRNN). Both models learn acoustic features of syllables via a CNN and temporal correlations of syllables into phrases either via an HMM or recurrent network. We compare their performance to commonly used CNNs LeNet and VGGNet, and support vector machine (SVM). We also propose a dynamic programming method to evaluate how well phrases are predicted. This is useful for evaluating performance when vocalisations are labelled by phrases, not syllables. 3. Our methods perform substantially better than the commonly used methods when applied to the gibbon acoustic recordings. The CRNN has an F-score of 90% on phrase prediction, which is 18% higher than the best of the SVM or LeNet and VGGNet methods. HMM post-processing raised the F-score of these last three methods to as much as 87%. The number of phrases is overestimated by CNNs and SVM, leading to error rates between 49% and 54%. With HMM, these error rates can be reduced to 0.4% at the lowest. Similarly, the error rate of CRNN's prediction is no more than 0.5%. 4. CRNNs are better at identifying phrases of varying lengths composed of a varying number of syllables than simpler CNN or SVM models. We find a CRNN model to be best at this task, with a CNN combined with an HMM performing almost as well. We recommend that these kinds of models are used for species whose vocalisations are structured into phrases of varying lengths.Publisher PDFPeer reviewe

WASIS - Identificação bioacústica de espécies baseada em múltiplos algoritmos de extração de descritores e de classificação

Orientador: Claudia Maria Bauzer MedeirosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A identificação automática de animais por meio de seus sons é um dos meios para realizar pesquisa em bioacústica. Este domínio de pesquisa fornece, por exemplo, métodos para o monitoramento de espécies raras e ameaçadas, análises de mudanças em comunidades ecológicas, ou meios para o estudo da função social de vocalizações no contexto comportamental. Mecanismos de identificação são tipicamente executados em dois estágios: extração de descritores e classificação. Ambos estágios apresentam desafios, tanto em ciência da computação quanto na bioacústica. A escolha de algoritmos de extração de descritores e técnicas de classificação eficientes é um desafio em qualquer sistema de reconhecimento de áudio, especialmente no domínio da bioacústica. Dada a grande variedade de grupos de animais estudados, algoritmos são adaptados a grupos específicos. Técnicas de classificação de áudio também são sensíveis aos descritores extraídos e condições associadas às gravações. Como resultado, muitos sistemas computacionais para bioacústica não são expansíveis, limitando os tipos de experimentos de reconhecimento que possam ser conduzidos. Baseado neste cenário, esta dissertação propõe uma arquitetura de software que acomode múltiplos algoritmos de extração de descritores, fusão entre descritores e algoritmos de classificação para auxiliar cientistas e o grande público na identificação de animais através de seus sons. Esta arquitetura foi implementada no software WASIS, gratuitamente disponível na Internet. Diversos algoritmos foram implementados, servindo como base para um estudo comparativo que recomenda conjuntos de algoritmos de extração de descritores e de classificação para três grupos de animaisAbstract: Automatic identification of animal species based on their sounds is one of the means to conduct research in bioacoustics. This research domain provides, for instance, ways to monitor rare and endangered species, to analyze changes in ecological communities, or ways to study the social meaning of the animal calls in the behavior context. Identification mechanisms are typically executed in two stages: feature extraction and classification. Both stages present challenges, in computer science and in bioacoustics. The choice of effective feature extraction and classification algorithms is a challenge on any audio recognition system, especially in bioacoustics. Considering the wide variety of animal groups studied, algorithms are tailored to specific groups. Classification techniques are also sensitive to the extracted features, and conditions surrounding the recordings. As a results, most bioacoustic softwares are not extensible, therefore limiting the kinds of recognition experiments that can be conducted. Given this scenario, this dissertation proposes a software architecture that allows multiple feature extraction, feature fusion and classification algorithms to support scientists and the general public on the identification of animal species through their recorded sounds. This architecture was implemented by the WASIS software, freely available on the Web. A number of algorithms were implemented, serving as the basis for a comparative study that recommends sets of feature extraction and classification algorithms for three animal groupsMestradoCiência da ComputaçãoMestre em Ciência da Computação132849/2015-12013/02219-0CNPQFAPES

Identificação automática de aves a partir de áudio

Bird classification from audio is mainly useful for ornithologists and ecologists. With growing amounts of data, manual bird classification is time-consuming, which makes it a costly method. Birds react quickly to environmental changes, which makes their analysis an important problem in ecology, as analyzing bird behaviour and population trends helps detect other organisms in the environment. A reliable methodology that automatically identifies bird species from audio would be a valuable tool for the experts in the area. The main purpose of this work is to propose a methodology able to identify a bird species by its chirp. There are many techniques that can be used to process the audio data, and to classify the audio data. This thesis explores the deep learning techniques that are being used in this domain, such as using Convolutional Neural Networks and Recurrent Neural Networks to classify the data. Audio problems in deep learning are commonly approached by converting them into images using feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients. Multiple deep learning and feature extraction combinations are used and compared in this thesis in order to find the most suitable approach to this problem.Classificação de pássaros a partir de áudio é principalmente útil para ornitólogos e ecologistas. Com o aumento da quantidade de dados disponível, classificar a espécie dos pássaros manualmente acaba por consumir muito tempo. Os pássaros reagem rapidamente às alterações climáticas, o que faz com que a análise de pássaros seja um problema interessante na ecologia, porque ao analisar o comportamento das aves e a tendência populacional, outros organismos podem ser detetados no meio ambiente. Devido a estes factos, a criação de uma metodologia que identifique a espécie dos pássaros fiavelmente seria uma ferramenta bastante útil para os especialistas na área. O objetivo principal do trabalho nesta dissertação é propor uma metodologia que identifique a espécie de uma ave através do seu canto. Existem diversas técnicas que podem ser usadas para processar os dados sonoros que contêm os cantos das aves, e que podem ser usadas para classificar as espécies das aves. Esta dissertação explora as principais técnicas de deep learning que são usadas neste domínio, tais como as redes neuronais convolucionais e as redes neuronais recorrentes que são usadas para classificar os dados. Os problemas relacionados com som no deep learning, são normalmente abordados por converter os dados sonoros em imagens utilizando técnicas de extração de atributos, para depois serem classificados utilizando modelos de deep learning tipicamente utilizados para classificar imagens. Dois exemplos destas técnicas de extração de atributos normalmente utilizadas são os Espectrogramas de Mel e os Coeficientes Cepstrais da Frequência de Mel. Nesta dissertação, são feitas múltiplas combinações de técnicas de deep learning com técnicas de extração de atributos do som. Estas combinações são utilizadas para serem comparadas com o âmbito de encontrar a abordagem mais apropriada para o problema