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The novel use of machine learning (ML) in the field of underwater acoustic communication (UAC) is explored in this research study, with an emphasis on the categorization of various underwater noises. Designing a communication system with the ability to successfully send information while evading detection is the major goal to increase the security and effectiveness of underwater communication. The study explores the difficulties brought forth by the complex undersea environment and describes how ML may be used to get around these constraints. It gives a thorough analysis of several machine learning (ML) methods with a focus on Deep Learning (DL) and how well they simulate the underwater audio channel. The study's results show how machine learning may improve UAC system performance, enabling safer and more effective underwater communication. To categorize different forms of natural noise present in the depths of aquatic ecosystems, a range of machine learning methods, including Convolutional Neural Network (CNN) and Multi Scale Deep Features Aggregation (MSDFA), are used in this study. The significance of these discoveries are discussed in the paper's conclusion, and it offers suggestions for further study in this fascinating area.
Illustration provides a graphical representation of the data visualization associated with the various machine learning models.
Illustration show comparison of different machine learning Models.
The heat map of the confusion matrix MSDFA, DenseNet, CNN, and Residual network method.
The motivation for "Convert Underwater Acoustic communication mimicking sea natural noise of environment using multi-scale deep features aggregation convolutional neural network". Using the multi-scale deep feature is expected to be effective for representing characteristic differences among different acoustic communication mimicking sea natural noise and thus to obtain a better result compared to single-scale deep feature and hand-crafted features. Mel-frequency Cepstral Coefficient (MFCC) is adopted as the input of CNNs for extracting multi-scale deep features due to its excellent performance in most audio signal processing tasks. CNN is used to produce multi-scale deep features because of its powerful ability to learn discriminative information from its input data by using some effective operations, such as convolution, pooling, and so on.
We used a particular dataset for our study, the Megaptera Novaeangliae Humpback Whale dataset. Due to its extensive collection of acoustic waves connected to the Humpback Whale species, this dataset is especially noteworthy since it offers a rich supply of data for our machine learning model. The dataset contains a wide range of unique classes and traits that were essential to the experimental procedure described in this study. These classifications stand in for the various geographic regions where the audio data were gathered.
Representation of Humpback whale sounds in 1-1.5 miles North of Tortola, British Virgin Islands region