Classification of bird sounds as an early warning method of forest fires using Convolutional Neural Network (CNN) algorithm

Permana, Silvester Dian Handy; Saputra, Gusti; Arifitama, Budi; Yaddarabullah,; Caesarendra, Wahyu; Rahim, Robbi

doi:10.1016/j.jksuci.2021.04.013

Cited by 43 publications

(8 citation statements)

References 13 publications

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“…Based on the TAR and TRR performance on Automolus rubiginosus, Synallaxis erythrothorax, Cardinalis, Cercomacra Tyrannina, and Myiozetetes Similis the result of the IMFCC method provide a percentage increase than MFCC. Finally, study conducted by [26], with the theme of a forest fire early warning system with the sound of birds. In this study, the bird data used in the form of recordings of bird sounds from four bird species.…”

Section: Classification Of Animal's Soundsmentioning

confidence: 99%

Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

Permana,

Rahman

2024

IJACSA

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The Javanese Eagle is a rare and protected animal in Indonesia. These animals only live in a few species and are threatened with extinction. These birds need to be bred to avoid extinction. One form of communication between the Javanese eagles and each other is the sound of their tweets. These tweets can be studied and classified to conserve endangered animals. This study will classify the sound of the Javanese Eagles for the benefit of animal conservation. Data in the form of voice tweets will be classified. This classification uses algorithms from improved MFCC (Mel-Frequency Cepstral Coefficients) and Deep Convolutional Neural Network. The result of this study was to classify the sound of the Javanese Eagle from the lack of food or drink, the normal tweets state of the bird, and to find out the Javanese Eagle in finding a partner. This research has been carried out by comparing the CNN architecture with AlexNet and VGGNet models and various combinations of training, validation, and test data. The best model dataset underwent division into 80% for training, 10% for validation, and 10% for testing. Training and testing of both IMFCC and VGGNet models occurred using the same dataset. During training, VGGNet achieved 100% accuracy, while testing yielded 99%. ROC Curve: 'Normal' AUC 0.996, 'Looking for Partner' AUC 1.000, 'Looking for Food' AUC 0.996. This study aids Javanese Eagle conservation, crucial for preventing extinction at conservation sites.

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Section: Classification Of Animal's Soundsmentioning

confidence: 99%

Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

Permana,

Rahman

2024

IJACSA

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“…However, understanding spectrograms is a challenge for biologists requiring effort to train researchers to detect species. This necessity of rigorous training to achieve high accuracy in classifying avians led to an opportunity for the application of deep learning methods , Ruff et al, 2020, Ruff et al, 2021, Hidayat et al, 2021, Permana et al, 2021, Bravo Sanchez et al, 2021. Brazilian biomes represent excellent opportunities to study the application of these methods given the vast diversity of avian species.…”

Section: Abstract: Soundscape Spectrogram Deep Learning Machine Visio...mentioning

confidence: 99%

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Aleem¹,

Kumar²,

Little³

et al. 2022

24th Irish Machine Vision and Image Processing Conference

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Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learning algorithms is highly dependent on data quality. Particularly for the medical domain, it is an important aspect as medical data is very sensitive to quality and poor quality can lead to misdiagnosis. To improve the diagnostic performance, research has been done both in complex DL architectures and in improving data quality using dataset dependent static hyperparameters. However, the performance is still constrained due to data quality and overfitting of hyperparameters to a specific dataset. To overcome these issues, this paper proposes random data augmentation based enhancement. The main objective is to develop a generalized, data-independent and computationally efficient enhancement approach to improve medical data quality for DL. The quality is enhanced by improving the brightness and contrast of images. In contrast to the existing methods, our method generates enhancement hyperparameters randomly within a defined range, which makes it robust and prevents overfitting to a specific dataset. To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks. For grayscale imagery, experiments have been performed with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets. Experimental results demonstrate that with the proposed enhancement methodology, DL architectures outperform other existing methods. Our code is publicly available at: https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement.

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“…Deep Learning has been applied in soundscape ecology, zoology and ethology research projects were primarily interested in species identification [Selin et al, 2006, Chou et al, 2007, Sprengel et al, 2016, Lasseck, 2018a, Christin et al, 2018, Sankupellay and Konovalov, 2018, Lasseck, 2018b, Zhang et al, 2019, Koh et al, 2019, Ruff et al, 2020, LeBien et al, 2020, Ruff et al, 2021, Huang and Basanta, 2021, Campos Paula et al, 2022. Widely used algorithms in this context are Deep Neural Networks and Convolutional Neural Networks (CNNs) [Ruff et al, 2020, Christin et al, 2018, Zhang et al, 2019, Ruff et al, 2021, Hidayat et al, 2021, Kahl et al, 2021, Permana et al, 2021, Disabato et al, 2021. Over the last year, studies showed good Deep Learning performances on avian species identification based on their sounds using mainly the Deep Neural Networks and CNN architectures [Kahl et al, 2021, Ruff et al, 2021, Hidayat et al, 2021, Permana et al, 2021.…”

Section: State Of the Artmentioning

confidence: 99%

“…Widely used algorithms in this context are Deep Neural Networks and Convolutional Neural Networks (CNNs) [Ruff et al, 2020, Christin et al, 2018, Zhang et al, 2019, Ruff et al, 2021, Hidayat et al, 2021, Kahl et al, 2021, Permana et al, 2021, Disabato et al, 2021. Over the last year, studies showed good Deep Learning performances on avian species identification based on their sounds using mainly the Deep Neural Networks and CNN architectures [Kahl et al, 2021, Ruff et al, 2021, Hidayat et al, 2021, Permana et al, 2021. These researchers focussed their effort on classifying species and applying different image pre-processing techniques.…”

Section: State Of the Artmentioning

confidence: 99%

On the Feasibility of Privacy-Secured Facial Authentication for low-power IoT Devices - Quantifying the Effects of Head Pose Variation on End-to-End Neural Face Recognition

Wang¹,

Varkarakis²,

Lemley³

et al. 2022

24th Irish Machine Vision and Image Processing Conference

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Recent low-power neural accelerator hardware provides a solution for end-to-end privacy and secure facial authentication, such as smart refueling machine locks in shared accommodation, smart speakers, or televisions that respond only to family members. This work explores the impact that head pose variation has on the performance of a state-of-the-art face recognition model. A synthetic technique is employed to introduce head pose variation into data samples. Experiments show that the synthetic pose variations have a similar effect on face recognition performance as the real samples with pose variations. The impact of large variations of head poses on the face recognizer was then explored by further amplifying the angle of the synthetic head pose. It is found that the accuracy of the face recognition model deteriorates as the pose increases. After fine-tuning the network, the face recognition model achieves close to the accuracy of frontal faces in all pose variations, indicating that the face recognition model can be tuned to compensate for the effect of large poses.

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Classification of bird sounds as an early warning method of forest fires using Convolutional Neural Network (CNN) algorithm

Cited by 43 publications

References 13 publications

Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

On the Feasibility of Privacy-Secured Facial Authentication for low-power IoT Devices - Quantifying the Effects of Head Pose Variation on End-to-End Neural Face Recognition

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