Bird Audio Diarization with Faster R-CNN

Shrestha, Roman; Glackin, Cornelius; Wall, Julie; Cannings, Nigel

doi:10.1007/978-3-030-86362-3_34

Cited by 3 publications

(2 citation statements)

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“…Xeno-canto currently contains 789,761 recordings of approximately 11,026 species (12,359 subspecies). Thus, the balanced, medium-sized subset Bird Songs from Europe, consisting of 50 discrete European bird species with 43 high-quality natural recordings per species, was used for bird song classification in this work [19].…”

Section: Datasetmentioning

confidence: 99%

“…Therefore, we used the 10-fold method to test the model, repeating the process 10 times and reporting the average classification results. The method proposed in this paper was evaluated using a dataset of 50 bird sounds collected in [19]. We divide the dataset into 10 roughly identical subsets (except for the last one), with 1 subset used as the initial set for constructing the initial concept space and 9 subsets used as the training set for updating the concept spaces.…”

Section: Evaluation Rulementioning

confidence: 99%

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A Novel Concept-Cognitive Learning Method for Bird Song Classification

Lin,

Wen,

Liao

2023

Mathematics

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Bird voice classification is a crucial issue in wild bird protection work. However, the existing strategies of static classification are always unable to achieve the desired outcomes in a dynamic data stream context, as the standard machine learning approaches mainly focus on static learning, which is not suitable for mining dynamic data and has the disadvantages of high computational overhead and hardware requirements. Therefore, these shortcomings greatly limit the application of standard machine learning approaches. This study aims to quickly and accurately distinguish bird species by their sounds in bird conservation work. For this reason, a novel concept-cognitive computing system (C3S) framework, namely, PyC3S, is proposed for bird sound classification in this paper. The proposed system uses feature fusion and concept-cognitive computing technology to construct a Python version of a dynamic bird song classification and recognition model on a dataset containing 50 species of birds. The experimental results show that the model achieves 92.77% accuracy, 92.26% precision, 92.25% recall, and a 92.41% F1-Score on the given 50 bird datasets, validating the effectiveness of our PyC3S compared to the state-of-the-art stream learning algorithms.

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Section: Datasetmentioning

confidence: 99%

Section: Evaluation Rulementioning

confidence: 99%