Optimizing passive acoustic sampling of bats in forests

Passive acoustic methods are increasingly used in biodiversity research and monitoring programs because they are cost-effective and permit the collection of large datasets. However, the accuracy of the results depends on the bioacoustic characteristics of the focal taxa and their habitat use. In particular, this applies to bats which exhibit distinct activity patterns in three-dimensionally structured habitats such as forests. We assessed the performance of 21 acoustic sampling schemes with three temporal sampling patterns and seven sampling designs. Acoustic sampling was performed in 32 forest plots, each containing three microhabitats: forest ground, canopy, and forest gap. We compared bat activity, species richness, and sampling effort using species accumulation curves fitted with the clench equation. In addition, we estimated the sampling costs to undertake the best sampling schemes. We recorded a total of 145,433 echolocation call sequences of 16 bat species. Our results indicated that to generate the best outcome, it was necessary to sample all three microhabitats of a given forest location simultaneously throughout the entire night. Sampling only the forest gaps and the forest ground simultaneously was the second best choice and proved to be a viable alternative when the number of available detectors is limited. When assessing bat species richness at the 1-km(2) scale, the implementation of these sampling schemes at three to four forest locations yielded highest labor cost-benefit ratios but increasing equipment costs. Our study illustrates that multiple passive acoustic sampling schemes require testing based on the target taxa and habitat complexity and should be performed with reference to cost-benefit ratios. Choosing a standardized and replicated sampling scheme is particularly important to optimize the level of precision in inventories, especially when rare or elusive species are expected

Animal sound activity detection using multi-class support vector machines

On March 11th 2011, the whole world was taken aback by another tragic experience of Tsunami triggered by a magnitude 9.8 earthquake in Japan. Just few days after that, on March 25th 2011, another earthquake of magnitude 6.8 hit Myanmar deaths and destructions. Despite the loss incurred on properties and human being, available data show that relatively few numbers of animals died during most natural disasters. Prior to the occurrence of these disasters, available reports shows that animals do migrate to higher level or leave the areas en masse ahead of the event. Other related account show that animal sometimes behaves in unusual ways prior to the occurrence of these natural disasters. These overwhelming evidences point to the fact that animals might have the ability to sense impending natural disaster precursor signals ahead of time. This paper discusses the preliminary results obtained from the use of support vector machine (SVM) and Mel-frequency cepstral coefficients (MFCC) in the development of animal sound activity detection (ASAD) which is an integral part in the development of earthquake and natural disaster prediction using unusual animal behavior. The use of MFCC has been proposed for the features extraction stage while SVM has been proposed for classification of the extracted features. Preliminary results obtained shows that the MFCC and SVM can be used for features extraction and features classification respectively

Investigation into the Perceptually Informed Data for Environmental Sound Recognition

Environmental sound is rich source of information that can be used to infer contexts. With the rise in ubiquitous computing, the desire of environmental sound recognition is rapidly growing. Primarily, the research aims to recognize the environmental sound using the perceptually informed data. The initial study is concentrated on understanding the current state-of-the-art techniques in environmental sound recognition. Then those researches are evaluated by a critical review of the literature. This study extracts three sets of features: Mel Frequency Cepstral Coefficients, Mel-spectrogram and sound texture statistics. Two kinds machine learning algorithms are cooperated with appropriate sound features. The models are compared with a low-level baseline model. It also presents a performance comparison between each model with the high-level human listeners. The study results in sound texture statistics model performing the best classification by achieving 45.1% of accuracy based on support vector machine with radial basis function kernel. Another Mel-spectrogram model based on Convolutional Neural Network also provided satisfactory results and have received predictive results greater than the benchmark test

Reconocimiento automático de llamados de murciélagos en estado libre usando técnicas de machine learning

RESUMEN : En este trabajo se desarrolló una herramienta que extrae y procesa características acústicas de archivos de extensión zero-crossing o wav. La herramienta hace uso de metodologías de machine learning y permite el reconocimiento automático de los géneros de murciélagos molosus (Sonotipo 1) y myotis (Sonotipo 2) disminuyendo así el tiempo de procesamiento de datos al automatizar el proceso. La herramienta desarrollada para el análisis de los 2 tipos de archivos se materializa en 2 funciones desarrolladas en el software R. Estas funciones realizan el análisis de las características extraídas de los audios mediante el uso de redes neuronales con el algoritmo de back propagation. Estas funciones se generaron de forma que tomen todos los audios de un tipo (wav o zc) en un directorio y se le realice el análisis completo a todos los audios. El resultado generado es un archivo csv con la descripción de las características halladas y en que tiempos del audio se detectaron los sonotipos

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

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

Aperfeiçoamento das estratégias de amostragem e monitorização de comunidades de morcegos

Tese de Mestrado, Biologia da Conservação, 2021, Universidade de Lisboa, Faculdade de CiênciasA monitorização da vida selvagem tem-se desenvolvido consideravelmente nos últimos anos com as novas tecnologias de deteção acústica. Esta fornece diversas informações a partir dos sons produzidos pelos animais que vão desde a simples identificação da presença de espécies atá à sua abundância, posição e atividade. Entre as espécies monitorizadas acusticamente, destacam-se os morcegos que ecolocalizam. As populações de morcegos têm estado em declínio justificando a necessidade de monitorização para fins de conservação. O presente estudo tem como objetivo aperfeiçoar os métodos de amostragem, com estações acústicas, das comunidades de morcegos em três vertentes: 1) comparação entre a amostragem junto a pontos de água e em zonas secas; 2) comparação entre a amostragem com gravadores AudioMoth e Song Meter SM4BAT; 3) comparação entre amostragem usando estações de gravação duplas e individuais. Os resultados deste estudo sugerem que, numa região ou época seca, a amostragem de comunidades de morcegos utilizando estações acústicas e mais eficiente se estas forem colocadas junto de pontos de água, sendo estes os locais mais representativos. Quanto à comparação dos dois gravadores utilizados, foi identificada uma tendência na deteção do número de espécies superior com gravador SM4BAT, de maior custo, relativamente às detetadas com o AudioMoth, mais económico. Tendo em conta o elevado custo do SM4BAT, a sua escolha dependerá da avaliação entre o benefício na deteção e a limitação financeira do estudo. A utilização de um gravador AM extra por local de amostragem permitiu detetar um valor ligeiramente superior de riqueza específica de morcegos, somente para números reduzidos de gravadores e de noites de amostragem. Os resultados indicaram que só se justifica a colocação de dois gravadores por local se o número de locais disponíveis para a amostragem for reduzido e o número destes equipamentos não for limitado.Wildlife monitoring has developed considerably in recent years with new acoustic detection technologies. Recorded sounds produced by animals provide diverse information ranging from the simple identification of species to their abundance, position and activity. Among the species monitored acoustically, are the bats that echolocate. Bat populations have been declining, which justifies the need of their monitoring for conservation efforts. This study aims to improve sampling methods, with acoustic stations of bat communities following three approaches: 1) comparison between sampling near water points and in dry areas; 2) comparison between sampling with AudioMoth and Song Meter SM4BAT recorders; 3) comparison between sampling using dual and single recording stations. The results of the present study suggest that, in a dry region or season, sampling bat communities using acoustic stations is more efficient if these are placed near water points. These are the most representative locations. When comparing the two recorders used, a trend was identified in the detection of a higher number of species with the SM4BAT recorder, of higher cost, relative to those detected with the AudioMoth, which is more economical. Given the high cost of the SM4BAT, its choice will depend on the assessment between the benefit in detection and the financial limitation of the study. The use of an extra AudioMoth recorder per sampling site allowed the detection of a slightly higher value of bat specific richness, in the case of a reduced numbers of recorders and sampling nights. The results indicated that placing two recorders per site is only justified if the number of sites available for sampling is small and the number of these devices is not limited

Plataforma para recolha e identificação automática de espécies de pássaros utilizando registos áudio

A análise e modelação dos sons de animais com o fim de estudar a sua biodiversidade são possíveis graças a uma ciência multidisciplinar denominada bioacústica. Este trabalho tem como objetivo a construção de uma solução capaz de identificar automaticamente as espécies de pássaros através do som emitido e ainda construir uma base de dados etiquetada com base nos registos áudio colhidos. Para a classificação dos sons dos pássaros, este trabalho segue duas metodologias para a extração de atributos e recorre a tarefas do data mining. Uma das metodologias de extração de atributos baseia-se na descoberta dos padrões mais frequentes no som, denominado (Motifs). A outra metodologia bastante usada em reconhecimento de sons, baseia-se em coeficientes Mel-Frequency (MFCCs). Para a classificação são usados algoritmos de árvores de decisão e Support Vector Machines. Foram também avaliadas diferentes metodologias de pré-processamento dos sons. Para a realização das experiências, foi utilizado o dataset disponibilizado pela Neural Information Processing Scaled for Bioacustic Bird song competition (NIPS4B). Nas experiências realizadas, o processo de classificação utilizando técnicas de normalização do sinal original no pré-processamento, a extração de atributos usando a abordagem MFCCs e o algoritmo de classificação Random Forest apresentou melhores resultados. Neste trabalho foi também desenvolvida uma aplicação móvel para o sistema operativo Android capaz de gravar os sons e identifica-los utilizando os recursos da aplicação servidor (web), com um tempo médio de resposta de 20 segundos.The analysis and modeling of animal sounds in order to study its biodiversity are possible thanks to a multidisciplinary science called bioacoustics. This work aims to build a client / server platform able to automatically identify the species of birds through the emitted sound and still build a database labeled based on the collected audio recordings. For the classification of the sounds of the birds, this work follows two methods for the extraction of attributes and uses of data mining tasks. One feature extraction methodology, is based on the discovery of the most frequent patterns in sound, called (Motifs). The other method, often used for sound recognition is based on Mel-Frequency coefficients (MFCCs). For the classification are used decision trees algorithms and Support Vector Machines. They were also evaluated different methods for pre-processing of sounds. To carry out the experiments, we used the dataset provided by the Neural Information Processing Scaled for Bioacustic Bird song competition (NIPS4B). In the experiments conducted, the classification process using normalization techniques of the original signal in the pre-processing, extracting attributes using the MFCCs approach and Random Forest classification algorithm showed the best results. This work also developed a mobile application for the operating system Android able to record sounds and identify them using the resources of the application server, with a response average time of 20 seconds