Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise
Snapping Shrimps (SSs) live in warm marine areas. Snapping Shrimps Noise (SSN), loud sounds generated by these underwater creatures, serves as a major source of in performance degradation by decreasing the Signal-to-Noise Ratio (SNR) for underwater acoustic communication and target detection. Thus,...
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2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| author | Suhyeon Park Jongwon Seok Jungpyo Hong |
| author_facet | Suhyeon Park Jongwon Seok Jungpyo Hong |
| author_sort | Suhyeon Park |
| collection | DOAJ |
| description | Snapping Shrimps (SSs) live in warm marine areas. Snapping Shrimps Noise (SSN), loud sounds generated by these underwater creatures, serves as a major source of in performance degradation by decreasing the Signal-to-Noise Ratio (SNR) for underwater acoustic communication and target detection. Thus, we propose a unified solution for SSN detection and reduction in this paper. First, Signal Presence Probability (SPP) is calculated for SSN detection, and then the SPP is provided to Non-negative Matrix Factorization (NMF) as a weight for SSN reduction. In the proposed method, SPP acts as a key factor for SSN detection and reduction. To verify the effectiveness of the proposed method, the SAVEX-15 dataset, real ocean data containing SSN, is used. As a result of SSN detection, it was confirmed that SPP presented the highest performance in the Receiver Operating Characteristics curve, and we achieved 0.014 higher Area Under the Curve compared to competing methods. In addition, Continuous Wave and Linear Frequency Modulation signals were set as target signals and combined with the SAVEX-15 data for evaluation of noise reduction performance. As a result, the performance of the SPP-weighted NMF (WNMF) presented at least 2 dB higher SNR and SDR while maintaining less LSD compared to the Optimally Modified Log Spectral Amplitude estimator and NMF. |
| format | Article |
| id | doaj-art-c7299ff1812248978568f35ff920849c |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-c7299ff1812248978568f35ff920849c2025-01-24T13:36:50ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-011319610.3390/jmse13010096Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp NoiseSuhyeon Park0Jongwon Seok1Jungpyo Hong2Department of Information and Communication Engineering, Changwon National University, Changwon-si 51140, Republic of KoreaDepartment of Information and Communication Engineering, Changwon National University, Changwon-si 51140, Republic of KoreaDepartment of Information and Communication Engineering, Changwon National University, Changwon-si 51140, Republic of KoreaSnapping Shrimps (SSs) live in warm marine areas. Snapping Shrimps Noise (SSN), loud sounds generated by these underwater creatures, serves as a major source of in performance degradation by decreasing the Signal-to-Noise Ratio (SNR) for underwater acoustic communication and target detection. Thus, we propose a unified solution for SSN detection and reduction in this paper. First, Signal Presence Probability (SPP) is calculated for SSN detection, and then the SPP is provided to Non-negative Matrix Factorization (NMF) as a weight for SSN reduction. In the proposed method, SPP acts as a key factor for SSN detection and reduction. To verify the effectiveness of the proposed method, the SAVEX-15 dataset, real ocean data containing SSN, is used. As a result of SSN detection, it was confirmed that SPP presented the highest performance in the Receiver Operating Characteristics curve, and we achieved 0.014 higher Area Under the Curve compared to competing methods. In addition, Continuous Wave and Linear Frequency Modulation signals were set as target signals and combined with the SAVEX-15 data for evaluation of noise reduction performance. As a result, the performance of the SPP-weighted NMF (WNMF) presented at least 2 dB higher SNR and SDR while maintaining less LSD compared to the Optimally Modified Log Spectral Amplitude estimator and NMF.https://www.mdpi.com/2077-1312/13/1/96non-negative matrix factorization (NMF)probabilistic noise interval detectionsnapping shrimpsunderwater noise |
| spellingShingle | Suhyeon Park Jongwon Seok Jungpyo Hong Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise Journal of Marine Science and Engineering non-negative matrix factorization (NMF) probabilistic noise interval detection snapping shrimps underwater noise |
| title | Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise |
| title_full | Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise |
| title_fullStr | Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise |
| title_full_unstemmed | Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise |
| title_short | Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise |
| title_sort | probabilistic noise detection and weighted non negative matrix factorization based noise reduction methods for snapping shrimp noise |
| topic | non-negative matrix factorization (NMF) probabilistic noise interval detection snapping shrimps underwater noise |
| url | https://www.mdpi.com/2077-1312/13/1/96 |
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