Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution
Abstract Birds are a kind of environmental indicator organism, which can reflect the changes in the ecological environment and biodiversity, and recognition of birdsongs can further help understand and protect birds and natural environment. Extreme learning machine (ELM) has the advantages of fast l...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-13957-w |
| _version_ | 1818236832974897152 |
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| author | Shanshan Xie Yan Zhang Danjv Lv Haifeng Xu Jiang Liu Yue Yin |
| author_facet | Shanshan Xie Yan Zhang Danjv Lv Haifeng Xu Jiang Liu Yue Yin |
| author_sort | Shanshan Xie |
| collection | DOAJ |
| description | Abstract Birds are a kind of environmental indicator organism, which can reflect the changes in the ecological environment and biodiversity, and recognition of birdsongs can further help understand and protect birds and natural environment. Extreme learning machine (ELM) has the advantages of fast learning speed and good generalization ability, which is widely used in classification and recognition problems. Input layer weights and hidden layer thresholds are two key factors affecting ELM performance. As one of swarm intelligence optimization methods, differential evolution (DE) can be used to optimize the parameters of ELM. In order to enhance the diversity, convergence speed and global search ability of the DE population, and improve the accuracy and stability of the classification model, this paper proposes a multi-strategy differential evolution method (M-SDE) to optimize the parameters of the ELM. And the differential MFCC feature parameters, extracted from birdsongs, are applied to build classification models of M-SDE_ELM and an ensemble M-SDE_EnELM with optimized ELM for bird species recognition. In the experiments, the ELM models optimized by the swarm intelligence algorithms PSO and GOA are compared and analyzed by hypothesis tests with the M-SDE_ELM and M-SDE_EnELM. Results show that the M-SDE_ELM and M-SDE_EnELM can achieve a classification accuracy of 86.70% and 89.05% in the classification of nine species of birds respectively, and the recognition effect and stability of the M-SDE_EnELM model outperform other models. |
| first_indexed | 2024-12-12T12:16:08Z |
| format | Article |
| id | doaj.art-73f022bd877e4b008ef6a4b4db4a1227 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T12:16:08Z |
| publishDate | 2022-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-73f022bd877e4b008ef6a4b4db4a12272022-12-22T00:24:45ZengNature PortfolioScientific Reports2045-23222022-06-0112111610.1038/s41598-022-13957-wBirdsongs recognition based on ensemble ELM with multi-strategy differential evolutionShanshan Xie0Yan Zhang1Danjv Lv2Haifeng Xu3Jiang Liu4Yue Yin5College of Big Data and Intelligent Engineering, Southwest Forestry UniversityCollege of Mathematics and Physics, Southwest Forestry UniversityCollege of Big Data and Intelligent Engineering, Southwest Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversityCollege of Big Data and Intelligent Engineering, Southwest Forestry UniversityCollege of Big Data and Intelligent Engineering, Southwest Forestry UniversityAbstract Birds are a kind of environmental indicator organism, which can reflect the changes in the ecological environment and biodiversity, and recognition of birdsongs can further help understand and protect birds and natural environment. Extreme learning machine (ELM) has the advantages of fast learning speed and good generalization ability, which is widely used in classification and recognition problems. Input layer weights and hidden layer thresholds are two key factors affecting ELM performance. As one of swarm intelligence optimization methods, differential evolution (DE) can be used to optimize the parameters of ELM. In order to enhance the diversity, convergence speed and global search ability of the DE population, and improve the accuracy and stability of the classification model, this paper proposes a multi-strategy differential evolution method (M-SDE) to optimize the parameters of the ELM. And the differential MFCC feature parameters, extracted from birdsongs, are applied to build classification models of M-SDE_ELM and an ensemble M-SDE_EnELM with optimized ELM for bird species recognition. In the experiments, the ELM models optimized by the swarm intelligence algorithms PSO and GOA are compared and analyzed by hypothesis tests with the M-SDE_ELM and M-SDE_EnELM. Results show that the M-SDE_ELM and M-SDE_EnELM can achieve a classification accuracy of 86.70% and 89.05% in the classification of nine species of birds respectively, and the recognition effect and stability of the M-SDE_EnELM model outperform other models.https://doi.org/10.1038/s41598-022-13957-w |
| spellingShingle | Shanshan Xie Yan Zhang Danjv Lv Haifeng Xu Jiang Liu Yue Yin Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution Scientific Reports |
| title | Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution |
| title_full | Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution |
| title_fullStr | Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution |
| title_full_unstemmed | Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution |
| title_short | Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution |
| title_sort | birdsongs recognition based on ensemble elm with multi strategy differential evolution |
| url | https://doi.org/10.1038/s41598-022-13957-w |
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