Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet Transform
The time–frequency characteristics of dolphin whistle signals under diverse ecological conditions and during environmental changes are key research topics that focus on the adaptive and response mechanisms of dolphins to the marine environment. To enhance the quality and utilization of passive acous...
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/2/312 |
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| author | Xiang Zhou Ru Wu Wen Chen Meiling Dai Peibin Zhu Xiaomei Xu |
| author_facet | Xiang Zhou Ru Wu Wen Chen Meiling Dai Peibin Zhu Xiaomei Xu |
| author_sort | Xiang Zhou |
| collection | DOAJ |
| description | The time–frequency characteristics of dolphin whistle signals under diverse ecological conditions and during environmental changes are key research topics that focus on the adaptive and response mechanisms of dolphins to the marine environment. To enhance the quality and utilization of passive acoustic monitoring (PAM) recorded dolphin whistles, the challenges faced by current wavelet thresholding methods in achieving precise threshold denoising under low signal-to-noise ratio (SNR) are confronted. This paper presents a thresholding denoising method based on stationary wavelet transform (SWT), utilizing suppression impulsive and autocorrelation function (SI-ACF) to select precise thresholds. This method introduces a denoising metric <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula>, based on the correlation of whistle signals, which facilitates precise threshold estimation under low SNR without requiring prior information. Additionally, it exploits the high amplitude and broadband characteristics of impulsive noise, and utilizes the multi-resolution information of the wavelet domain to remove impulsive noise through a multi-level sliding window approach. The SI-ACF method was validated using both simulated and real whistle datasets. Simulated signals were employed to evaluate the method’s denoising performance under three types of typical underwater noise. Real whistles were used to confirm its applicability in real scenarios. The test results show the SI-ACF method effectively eliminates noise, improves whistle signal spectrogram visualization, and enhances the accuracy of automated whistle detection, highlighting its potential for whistle signal preprocessing under low SNR. |
| format | Article |
| id | doaj-art-c9c52b831e484120866e8be9fac6ade8 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-c9c52b831e484120866e8be9fac6ade82025-08-20T02:44:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113231210.3390/jmse13020312Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet TransformXiang Zhou0Ru Wu1Wen Chen2Meiling Dai3Peibin Zhu4Xiaomei Xu5School of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marxism, Jimei University, Xiamen 361021, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen 361021, ChinaCollege of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, ChinaThe time–frequency characteristics of dolphin whistle signals under diverse ecological conditions and during environmental changes are key research topics that focus on the adaptive and response mechanisms of dolphins to the marine environment. To enhance the quality and utilization of passive acoustic monitoring (PAM) recorded dolphin whistles, the challenges faced by current wavelet thresholding methods in achieving precise threshold denoising under low signal-to-noise ratio (SNR) are confronted. This paper presents a thresholding denoising method based on stationary wavelet transform (SWT), utilizing suppression impulsive and autocorrelation function (SI-ACF) to select precise thresholds. This method introduces a denoising metric <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula>, based on the correlation of whistle signals, which facilitates precise threshold estimation under low SNR without requiring prior information. Additionally, it exploits the high amplitude and broadband characteristics of impulsive noise, and utilizes the multi-resolution information of the wavelet domain to remove impulsive noise through a multi-level sliding window approach. The SI-ACF method was validated using both simulated and real whistle datasets. Simulated signals were employed to evaluate the method’s denoising performance under three types of typical underwater noise. Real whistles were used to confirm its applicability in real scenarios. The test results show the SI-ACF method effectively eliminates noise, improves whistle signal spectrogram visualization, and enhances the accuracy of automated whistle detection, highlighting its potential for whistle signal preprocessing under low SNR.https://www.mdpi.com/2077-1312/13/2/312dolphin whistlestationary wavelet transformbioacousticsimpulsive noisewavelet thresholdingautocorrelation function |
| spellingShingle | Xiang Zhou Ru Wu Wen Chen Meiling Dai Peibin Zhu Xiaomei Xu Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet Transform Journal of Marine Science and Engineering dolphin whistle stationary wavelet transform bioacoustics impulsive noise wavelet thresholding autocorrelation function |
| title | Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet Transform |
| title_full | Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet Transform |
| title_fullStr | Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet Transform |
| title_full_unstemmed | Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet Transform |
| title_short | Thresholding Dolphin Whistles Based on Signal Correlation and Impulsive Noise Features Under Stationary Wavelet Transform |
| title_sort | thresholding dolphin whistles based on signal correlation and impulsive noise features under stationary wavelet transform |
| topic | dolphin whistle stationary wavelet transform bioacoustics impulsive noise wavelet thresholding autocorrelation function |
| url | https://www.mdpi.com/2077-1312/13/2/312 |
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