Continuous Automatic Bioacoustics Monitoring of Bird Calls with Local Processing on Node Level

Weerasena, Hansika; Jayawardhana, Manesh; Egodage, D. N.; Fernando, Heshan; Sooriyaarachchi, Sulochana; Gamage, Chandana; Kottege, Navinda

doi:10.1109/tencon.2018.8650196

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Já [B. T. Padovese 2019] utlizou um Multilayer Perceptron para monitoramento do pássaro da amazônia brasileira, a Rhodocorytha, alcanc ¸ando resultados de até 98% de acurácia. Residual Neural Network [Xiao et al 2022] e [Hu et al 2023] Convolutional Neural Network [Hidayat et al 2021] e [Maegawa et al 2021] Support Vector Machine (SVM) [Weerasena et al 2018] Spiking Neural Network [Mohanty et al 2023] Naive Bayes Classifier [Tivarekar et al 2018] Recurrent Neural Network [Xie et al 2020] Em [Yang et al 2022] foi proposto um modelo de reconhecimento de pássaros usando a arquitetura Lightweight Neural Networks (LWN) que são um subconjunto das Redes de Peso Contínuo convencionais (CWN). Foi usado um conjunto de dados de Mel Spectrogram Min-Max Standardization, alcanc ¸ando uma acurácia de 95,12%.…”

Section: Resultados E Discussõesunclassified

“…Em [Maegawa et al 2021], foi apresentada uma ferramenta para acompanhamento de uma espécie de pássaro com o uso de CNN e com acurácia geral de 97% para este sistema. [Weerasena et al 2018] utilizam Suport Vector Machine (SVM) para criac ¸ão de um classificador local, para funcionar junto aos dispositivos de captura em campo. O sistema foi validado com 93,5% de acurácia utilizando 214 arquivos de som de 5 espécies.…”

Section: Resultados E Discussõesunclassified

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Revisão Sistemática sobre Machine Learning Aplicada a Bioacústica utilizando o Método PRISMA

Martins,

dos Santos,

de Oliveira

et al. 2023

Anais Da XII Escola Regional De Informática De Mato Grosso (ERI-MT 2023)

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O uso da técnica mais adequada é crucial em qualquer processo Aprendizagem de Máquina. Na bioacústica, devido à complexidade dos dados muitas técnicas tem sido aplicadas e desenvolvidas. Nesse contexto, esse trabalho apresenta a revisão sistemática realizada utilizando a metodologia PRISMA para Bioacústica no monitoramento ambiental por meio de vocalização de pássaros. A revisão demonstrou que as técnicas de Spiking Neural Network, Convolutional Neural Network e Residual Neural Network ganharam destaque nos últimos anos.

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Section: Resultados E Discussõesunclassified

Revisão Sistemática sobre Machine Learning Aplicada a Bioacústica utilizando o Método PRISMA

Martins,

dos Santos,

de Oliveira

et al. 2023

Anais Da XII Escola Regional De Informática De Mato Grosso (ERI-MT 2023)

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“…The most common approach is using CNN [1] [2]. Some of the approaches use machine learning [3], some use neural networks [4] while some combine machine learning and neural networks like combination of ANN(Arti cial Neural Network) and SVM [5] [6], combination of ANN and KNC(K-Nearest Centroid) where SVM and KNC are used for processing audio signals before feeding them into network. An approach using RNN was proposed, also combination of CNN and Recurrent Neural Network [7] model was proposed.…”

Section: Methodsmentioning

confidence: 99%

“…Convolution layers uses (5,5) two-dimensional convolution matrix keeping the padding same. Convolution layers of neural network are followed by max pooling layers with the lter size of (3,3).…”

Section: Model Architecturementioning

confidence: 99%

Bird Call Identification

Singh,

Mulyan

2024

Preprint

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The paper purposes an approach identify the bird species given their chirping sounds. The paper proposes Convolutional Neural Network architecture for classifying the species. In our proposed approach, we first convert the audio files to Mel spectrogram and use them for classification. We built a CNN model with three Convolutional layers, and then tuned parameters like number of epochs, learning rate, dropout percentage and size of convolutional matrix. The training dataset consists of 1485 audio clips containing 40 distinct species found across South Africa. The designed set of model achieved accuracy in the range of 32–52% on the test dataset.

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“…Cinkler et al have used SVM-based two-step classifiers to identify the sounds of birds on agricultural land and play a sound when birds are identified near the crops. In bioacoustics, SVM was used to classify healthy and unhealthy birds − . Researchers , have used SVM for avian influenza disease infected birds on broiler chicken farms.…”

Section: Role Of Emerging Technologies In Poultry Health Managementmentioning

confidence: 99%

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Goyal,

Yadav,

Mukherjee

2024

ACS Agric. Sci. Technol.

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The rapid growth of human population and frequent changes in environmental conditions pose challenges in providing nutrient-rich food to the current population. The demand for poultry products is increasing exponentially as they are a good source of low-cost proteins. Ensuring the well-being of birds and delivering nutritious poultry products is essential for both current and future food security needs and sustainable agriculture. This article is motivated by the potential of emerging digital technologies in agriculture to provide innovative solutions to challenges faced by farmers, particularly in poultry farming. It aims to shed light on various issues encountered by farmers like diseases on poultry farms and offers valuable insights for researchers seeking to address these challenges using the advancements in digital technologies. Precision farming in poultry involves leveraging technologies such as the internet of things, artificial intelligence, and edge computing to enhance animal health management. A smart poultry farm maintains the farm environment and detects diseases at early stage in the chickens. The farm resources are utilized optimally in the smart poultry farm. This research work provides a systematic literature review of intelligent systems designed for (i) poultry birds' health and welfare management using technologies like the internet of things (IoT), artificial intelligence, cloud computing, and edge computing and (ii) early disease detection, estimation of weight, and feeding behavior of birds with the use of computer vision and vocalization analysis. This article also explores various sensors employed in the development of IoT infrastructure. It has been demonstrated in the literature that modern digital technologies automate the management operations of poultry farms. As a result, the farmer's income increases, and high-quality food products are available to people at low cost.

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Continuous Automatic Bioacoustics Monitoring of Bird Calls with Local Processing on Node Level

Cited by 5 publications

References 17 publications

Revisão Sistemática sobre Machine Learning Aplicada a Bioacústica utilizando o Método PRISMA

Revisão Sistemática sobre Machine Learning Aplicada a Bioacústica utilizando o Método PRISMA

Bird Call Identification

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

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