Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet
Bird vocalizations are pivotal for ecological monitoring, providing insights into biodiversity and ecosystem health. Traditional recognition methods often neglect phase information, resulting in incomplete feature representation. In this paper, we introduce a novel approach to bird vocalization reco...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Avian Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2053716625000088 |
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| author | Jiangjian Xie Zhulin Hao Chunhe Hu Changchun Zhang Junguo Zhang |
| author_facet | Jiangjian Xie Zhulin Hao Chunhe Hu Changchun Zhang Junguo Zhang |
| author_sort | Jiangjian Xie |
| collection | DOAJ |
| description | Bird vocalizations are pivotal for ecological monitoring, providing insights into biodiversity and ecosystem health. Traditional recognition methods often neglect phase information, resulting in incomplete feature representation. In this paper, we introduce a novel approach to bird vocalization recognition (BVR) that integrates both amplitude and phase information, leading to enhanced species identification. We propose MHAResNet, a deep learning (DL) model that employs residual blocks and a multi-head attention mechanism to capture salient features from logarithmic power (POW), Instantaneous Frequency (IF), and Group Delay (GD) extracted from bird vocalizations. Experiments on three bird vocalization datasets demonstrate our method's superior performance, achieving accuracy rates of 94%, 98.9%, and 87.1% respectively. These results indicate that our approach provides a more effective representation of bird vocalizations, outperforming existing methods. This integration of phase information in BVR is innovative and significantly advances the field of automatic bird monitoring technology, offering valuable tools for ecological research and conservation efforts. |
| format | Article |
| id | doaj-art-23d080aaf7f742f38abc839214758d2a |
| institution | OA Journals |
| issn | 2053-7166 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Avian Research |
| spelling | doaj-art-23d080aaf7f742f38abc839214758d2a2025-08-20T02:02:12ZengKeAi Communications Co., Ltd.Avian Research2053-71662025-03-0116110022910.1016/j.avrs.2025.100229Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNetJiangjian Xie0Zhulin Hao1Chunhe Hu2Changchun Zhang3Junguo Zhang4School of Technology, Beijing Forestry University, Beijing, 100083, China; State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, 100083, China; Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing, 100083, China; Research Center for Biodiversity Intelligent Monitoring, Beijing Forestry University, Beijing, 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing, 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing, 100083, China; Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing, 100083, China; Research Center for Biodiversity Intelligent Monitoring, Beijing Forestry University, Beijing, 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing, 100083, China; Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing, 100083, China; Research Center for Biodiversity Intelligent Monitoring, Beijing Forestry University, Beijing, 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing, 100083, China; State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, 100083, China; Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing, 100083, China; Corresponding author. School of Technology, Beijing Forestry University, Beijing, 100083, China.Bird vocalizations are pivotal for ecological monitoring, providing insights into biodiversity and ecosystem health. Traditional recognition methods often neglect phase information, resulting in incomplete feature representation. In this paper, we introduce a novel approach to bird vocalization recognition (BVR) that integrates both amplitude and phase information, leading to enhanced species identification. We propose MHAResNet, a deep learning (DL) model that employs residual blocks and a multi-head attention mechanism to capture salient features from logarithmic power (POW), Instantaneous Frequency (IF), and Group Delay (GD) extracted from bird vocalizations. Experiments on three bird vocalization datasets demonstrate our method's superior performance, achieving accuracy rates of 94%, 98.9%, and 87.1% respectively. These results indicate that our approach provides a more effective representation of bird vocalizations, outperforming existing methods. This integration of phase information in BVR is innovative and significantly advances the field of automatic bird monitoring technology, offering valuable tools for ecological research and conservation efforts.http://www.sciencedirect.com/science/article/pii/S2053716625000088Bird vocalization recognitionFeature fusionPhase informationResidual network |
| spellingShingle | Jiangjian Xie Zhulin Hao Chunhe Hu Changchun Zhang Junguo Zhang Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet Avian Research Bird vocalization recognition Feature fusion Phase information Residual network |
| title | Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet |
| title_full | Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet |
| title_fullStr | Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet |
| title_full_unstemmed | Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet |
| title_short | Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet |
| title_sort | beyond amplitude phase integration in bird vocalization recognition with mharesnet |
| topic | Bird vocalization recognition Feature fusion Phase information Residual network |
| url | http://www.sciencedirect.com/science/article/pii/S2053716625000088 |
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