Nsalo Kong, D.F.; Shen, C.; Tian, C.; Zhang, S.R. Underwater Acoustic Monitoring: A Comprehensive Approach to Enhance MFCC Robustness and Classification Accuracy. Preprints2024, 2024010652. https://doi.org/10.20944/preprints202401.0652.v1
APA Style
Nsalo Kong, D.F., Shen, C., Tian, C., & Zhang, S.R. (2024). Underwater Acoustic Monitoring: A Comprehensive Approach to Enhance MFCC Robustness and Classification Accuracy. Preprints. https://doi.org/10.20944/preprints202401.0652.v1
Chicago/Turabian Style
Nsalo Kong, D.F., Chuan Tian and Sheng Rong Zhang. 2024 "Underwater Acoustic Monitoring: A Comprehensive Approach to Enhance MFCC Robustness and Classification Accuracy" Preprints. https://doi.org/10.20944/preprints202401.0652.v1
Abstract
Passive marine listening, involving the acoustic monitoring of underwater environments, has become increasingly vital for scientific research, environmental monitoring, and defense applications. However, the success of such applications critically depends on the ability to extract meaningful information from the often noisy and dynamic underwater acoustic environment. In this context, Mel-frequency cepstral coefficients (MFCCs) have emerged as an essential tool for feature extraction, capturing the spectral characteristics of marine sounds and making them ideal for species identification and sound event detection. This research presents an innovative approach to enhance the robustness of MFCC-based passive marine listening through adaptive noise reduction techniques. The proposed approach utilizes dynamic spectral subtraction to counter underwater noise, resulting in enhanced signal-to-noise ratios for desired sounds. This adaptive system adjusts to dynamic underwater noise, allowing it to concentrate on target sounds and suppress interference effectively. The experimentation further validates the effectiveness of the proposed approach, with results reaching 99% on the full dataset of 570 mammals and vessels, demonstrating the effectiveness of SVM and Random Forests in the classification of underwater audio.
Keywords
MFCC; spectral subtraction; svm; random forests; feature engineering
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
