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
Online-Zugang:	Volltext
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