Optimizing poultry audio signal classification with deep learning and burn layer fusion

This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness. The methodology integrates digital audio signal processing, convolutional neural networks (CNNs), and the innovative Burn Layer, which inje...

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Veröffentlicht in:	Journal of big data Jg. 11; H. 1; S. 135 - 29
Hauptverfasser:	Hassan, Esraa, Elbedwehy, Samar, Shams, Mahmoud Y., Abd El-Hafeez, Tarek, El-Rashidy, Nora
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2024 Springer Nature B.V SpringerOpen
Schlagworte:	Algorithms Animal health Animal husbandry Animals Artificial neural networks Audio data Audio signals Big Data Classification Communications Engineering Computational Science and Engineering Computer Science Convolutional neural networks Correlation coefficients Data augmentation Data Mining and Knowledge Discovery Database Management Deep learning Digital audio signal processing Disease detection Dropping out Information Storage and Retrieval Learning Machine learning Mathematical Applications in Computer Science Networks Neural networks Optimization Parameter robustness Parameter sensitivity Performance evaluation Poultry Poultry audio classification Random noise Resilience Robustness Sensitivity Signal analysis Signal classification Signal processing Training Deep learning Poultry audio classification Sensitivity Specificity Disease detection Convolutional neural networks Digital audio signal processing
ISSN:	2196-1115, 2196-1115
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Abstract	This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness. The methodology integrates digital audio signal processing, convolutional neural networks (CNNs), and the innovative Burn Layer, which injects controlled random noise during training to reinforce the model's resilience to input signal variations. The proposed architecture is streamlined, with convolutional blocks, densely connected layers, dropout, and an additional Burn Layer to fortify robustness. The model demonstrates efficiency by reducing trainable parameters to 191,235, compared to traditional architectures with over 1.7 million parameters. The proposed model utilizes a Burn Layer with burn intensity as a parameter and an Adamax optimizer to optimize and address the overfitting problem. Thorough evaluation using six standard classification metrics showcases the model's superior performance, achieving exceptional sensitivity (96.77%), specificity (100.00%), precision (100.00%), negative predictive value (NPV) (95.00%), accuracy (98.55%), F1 score (98.36%), and Matthew’s correlation coefficient (MCC) (95.88%). This research contributes valuable insights into the fields of audio signal processing, animal health monitoring, and robust deep-learning classification systems. The proposed model presents a systematic approach for developing and evaluating a deep learning-based poultry audio classification system. It processes raw audio data and labels to generate digital representations, utilizes a Burn Layer for training variability, and constructs a CNN model with convolutional blocks, pooling, and dense layers. The model is optimized using the Adamax algorithm and trained with data augmentation and early-stopping techniques. Rigorous assessment on a test dataset using standard metrics demonstrates the model's robustness and efficiency, with the potential to significantly advance animal health monitoring and disease detection through audio signal analysis.
AbstractList	This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness. The methodology integrates digital audio signal processing, convolutional neural networks (CNNs), and the innovative Burn Layer, which injects controlled random noise during training to reinforce the model's resilience to input signal variations. The proposed architecture is streamlined, with convolutional blocks, densely connected layers, dropout, and an additional Burn Layer to fortify robustness. The model demonstrates efficiency by reducing trainable parameters to 191,235, compared to traditional architectures with over 1.7 million parameters. The proposed model utilizes a Burn Layer with burn intensity as a parameter and an Adamax optimizer to optimize and address the overfitting problem. Thorough evaluation using six standard classification metrics showcases the model's superior performance, achieving exceptional sensitivity (96.77%), specificity (100.00%), precision (100.00%), negative predictive value (NPV) (95.00%), accuracy (98.55%), F1 score (98.36%), and Matthew’s correlation coefficient (MCC) (95.88%). This research contributes valuable insights into the fields of audio signal processing, animal health monitoring, and robust deep-learning classification systems. The proposed model presents a systematic approach for developing and evaluating a deep learning-based poultry audio classification system. It processes raw audio data and labels to generate digital representations, utilizes a Burn Layer for training variability, and constructs a CNN model with convolutional blocks, pooling, and dense layers. The model is optimized using the Adamax algorithm and trained with data augmentation and early-stopping techniques. Rigorous assessment on a test dataset using standard metrics demonstrates the model's robustness and efficiency, with the potential to significantly advance animal health monitoring and disease detection through audio signal analysis. Abstract This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness. The methodology integrates digital audio signal processing, convolutional neural networks (CNNs), and the innovative Burn Layer, which injects controlled random noise during training to reinforce the model's resilience to input signal variations. The proposed architecture is streamlined, with convolutional blocks, densely connected layers, dropout, and an additional Burn Layer to fortify robustness. The model demonstrates efficiency by reducing trainable parameters to 191,235, compared to traditional architectures with over 1.7 million parameters. The proposed model utilizes a Burn Layer with burn intensity as a parameter and an Adamax optimizer to optimize and address the overfitting problem. Thorough evaluation using six standard classification metrics showcases the model's superior performance, achieving exceptional sensitivity (96.77%), specificity (100.00%), precision (100.00%), negative predictive value (NPV) (95.00%), accuracy (98.55%), F1 score (98.36%), and Matthew’s correlation coefficient (MCC) (95.88%). This research contributes valuable insights into the fields of audio signal processing, animal health monitoring, and robust deep-learning classification systems. The proposed model presents a systematic approach for developing and evaluating a deep learning-based poultry audio classification system. It processes raw audio data and labels to generate digital representations, utilizes a Burn Layer for training variability, and constructs a CNN model with convolutional blocks, pooling, and dense layers. The model is optimized using the Adamax algorithm and trained with data augmentation and early-stopping techniques. Rigorous assessment on a test dataset using standard metrics demonstrates the model's robustness and efficiency, with the potential to significantly advance animal health monitoring and disease detection through audio signal analysis.
ArticleNumber	135
Author	Hassan, Esraa Abd El-Hafeez, Tarek Elbedwehy, Samar El-Rashidy, Nora Shams, Mahmoud Y.
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Keywords	Deep learning Poultry audio classification Sensitivity Specificity Disease detection Convolutional neural networks Digital audio signal processing
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Snippet	This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model... Abstract This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model...
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SubjectTerms	Algorithms Animal health Animal husbandry Animals Artificial neural networks Audio data Audio signals Big Data Classification Communications Engineering Computational Science and Engineering Computer Science Convolutional neural networks Correlation coefficients Data augmentation Data Mining and Knowledge Discovery Database Management Deep learning Digital audio signal processing Disease detection Dropping out Information Storage and Retrieval Learning Machine learning Mathematical Applications in Computer Science Networks Neural networks Optimization Parameter robustness Parameter sensitivity Performance evaluation Poultry Poultry audio classification Random noise Resilience Robustness Sensitivity Signal analysis Signal classification Signal processing Training
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Title	Optimizing poultry audio signal classification with deep learning and burn layer fusion
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