PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoring
Passive acoustic monitoring faces methodological challenges when isolating biological signals from anthropogenically dominated marine soundscapes. To address this, we present two novel computational workflows: (1) a Principal Component Analysis (PCA)-driven noise reduction algorithm that selectively...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002894 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233603018358784 |
|---|---|
| author | Bingjia Huang Yi Wu Yihua Lyu Xi Yan Mengmeng Tong Xiaoping Wang |
| author_facet | Bingjia Huang Yi Wu Yihua Lyu Xi Yan Mengmeng Tong Xiaoping Wang |
| author_sort | Bingjia Huang |
| collection | DOAJ |
| description | Passive acoustic monitoring faces methodological challenges when isolating biological signals from anthropogenically dominated marine soundscapes. To address this, we present two novel computational workflows: (1) a Principal Component Analysis (PCA)-driven noise reduction algorithm that selectively suppresses anthropogenic noise (e.g., vessel sounds) overlapping with biological frequency bands; and (2) an automatic Bio-voice Count Index (BCI) that quantifies target biological sounds through energy thresholding and adjustable frequency-weighting curve. We validated these methods using both synthetic soundscapes and 700 min of field recordings from coral reef ecosystems in Sanya, China. The PCA algorithm improved mean signal-to-noise ratios of field recordings by 5.3 dB (from 7.6 dB to 12.9 dB), effectively enhancing biological sound detectability. The BCI demonstrated strong correlations with biological metrics. When combined with the Acoustic Complexity Index, it improved the accuracy of fish abundance estimation compared to single-index approaches. Critically, our method reduces the analysis time by >90 % compared to manual methods. These tools provide ecologists with a reproducible framework for quantifying biodiversity in noisy environments, directly applicable to coral reef health monitoring and anthropogenic impact assessments. |
| format | Article |
| id | doaj-art-689dfe7ac96a465a976304c83d1fd3e6 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-689dfe7ac96a465a976304c83d1fd3e62025-08-20T05:05:32ZengElsevierEcological Informatics1574-95412025-12-019010328010.1016/j.ecoinf.2025.103280PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoringBingjia Huang0Yi Wu1Yihua Lyu2Xi Yan3Mengmeng Tong4Xiaoping Wang5Ocean College, Zhejiang University, Zhoushan 316021, China; Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510000, ChinaOcean College, Zhejiang University, Zhoushan 316021, China; The Fourth Institute of Oceanography, Ministry of Natural Resources, Beihai 536000, ChinaKey Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510000, China; Nansha Islands Coral Reef Ecosystem National Observation and Research Station, Guangzhou 510300, ChinaHangzhou UTSIS Technology Co., Ltd, Hangzhou 310000, ChinaOcean College, Zhejiang University, Zhoushan 316021, China; Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510000, China; The Fourth Institute of Oceanography, Ministry of Natural Resources, Beihai 536000, China; Hainan Institute, Zhejiang University, Sanya 572000, ChinaOcean College, Zhejiang University, Zhoushan 316021, China; Donghai Lab, Zhoushan, Zhejiang 316021, China; Hainan Institute, Zhejiang University, Sanya 572000, China; Corresponding author at: Ocean College, Zhejiang University, Zhoushan 316021, China.Passive acoustic monitoring faces methodological challenges when isolating biological signals from anthropogenically dominated marine soundscapes. To address this, we present two novel computational workflows: (1) a Principal Component Analysis (PCA)-driven noise reduction algorithm that selectively suppresses anthropogenic noise (e.g., vessel sounds) overlapping with biological frequency bands; and (2) an automatic Bio-voice Count Index (BCI) that quantifies target biological sounds through energy thresholding and adjustable frequency-weighting curve. We validated these methods using both synthetic soundscapes and 700 min of field recordings from coral reef ecosystems in Sanya, China. The PCA algorithm improved mean signal-to-noise ratios of field recordings by 5.3 dB (from 7.6 dB to 12.9 dB), effectively enhancing biological sound detectability. The BCI demonstrated strong correlations with biological metrics. When combined with the Acoustic Complexity Index, it improved the accuracy of fish abundance estimation compared to single-index approaches. Critically, our method reduces the analysis time by >90 % compared to manual methods. These tools provide ecologists with a reproducible framework for quantifying biodiversity in noisy environments, directly applicable to coral reef health monitoring and anthropogenic impact assessments.http://www.sciencedirect.com/science/article/pii/S1574954125002894Acoustic indexCoral reefEco acousticsPassive acoustic monitoringPrincipal component analysis |
| spellingShingle | Bingjia Huang Yi Wu Yihua Lyu Xi Yan Mengmeng Tong Xiaoping Wang PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoring Ecological Informatics Acoustic index Coral reef Eco acoustics Passive acoustic monitoring Principal component analysis |
| title | PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoring |
| title_full | PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoring |
| title_fullStr | PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoring |
| title_full_unstemmed | PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoring |
| title_short | PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoring |
| title_sort | pca based denoising and automatic recognition of marine biological sounds to estimate bio voice count index for marine monitoring |
| topic | Acoustic index Coral reef Eco acoustics Passive acoustic monitoring Principal component analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002894 |
| work_keys_str_mv | AT bingjiahuang pcabaseddenoisingandautomaticrecognitionofmarinebiologicalsoundstoestimatebiovoicecountindexformarinemonitoring AT yiwu pcabaseddenoisingandautomaticrecognitionofmarinebiologicalsoundstoestimatebiovoicecountindexformarinemonitoring AT yihualyu pcabaseddenoisingandautomaticrecognitionofmarinebiologicalsoundstoestimatebiovoicecountindexformarinemonitoring AT xiyan pcabaseddenoisingandautomaticrecognitionofmarinebiologicalsoundstoestimatebiovoicecountindexformarinemonitoring AT mengmengtong pcabaseddenoisingandautomaticrecognitionofmarinebiologicalsoundstoestimatebiovoicecountindexformarinemonitoring AT xiaopingwang pcabaseddenoisingandautomaticrecognitionofmarinebiologicalsoundstoestimatebiovoicecountindexformarinemonitoring |