Traditional Machine Learning and Deep Learning Modeling for Legume Species Recognition

Rimi, Iffat Firozy; Habib, Md. Tarek; Supriya, Soummo; Khan, Md. Abbas Ali; Hossain, Syed Akhter

doi:10.1007/s42979-022-01268-w

Cited by 9 publications

(1 citation statement)

References 12 publications

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“…This approach has several advantages over traditional methods, including the ability to process large amounts of data quickly and accurately, the ability to identify birds in real-time, and the ability to handle complex variations in bird vocalizations. Some of the most commonly used machine learning methods for bird species recognition include deep neural networks, support vector machines, and random forest [20,21]. However, the success of this approach also depends on the quality and quantity of the bird song data used for training, and on the effectiveness of the machine learning algorithms used for recognition.…”

Section: Introductionmentioning

confidence: 99%

Recognition of bird species with birdsong records using machine learning methods

Tang,

Liu,

Yuan

2024

PLoS ONE

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The recognition of bird species through the analysis of their vocalizations is a crucial aspect of wildlife conservation and biodiversity monitoring. In this study, the acoustic features of Certhia americana, Certhia brachydactyla, and Certhia familiaris were calculated including the Acoustic complexity index (ACI), Acoustic diversity index (ADI), Acoustic evenness index (AEI), Bioacoustic index (BI), Median of the amplitude envelop (MA), and Normalized Difference Soundscape Index (NDSI). Three machine learning models, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were constructed. The results showed that the XGBoost model had the best performance among the three models, with the highest accuracy (0.8365) and the highest AUC (0.8871). This suggests that XGBoost is an effective tool for bird species recognition based on acoustic indices. The study provides a new approach to bird species recognition that utilizes sound data and acoustic characteristics.

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Section: Introductionmentioning

confidence: 99%