In this article, we explore a new approach to underwater acoustic signal classification using a neural network called the Cascade method. The Cascade method is similar to a multi-level sorting system, where signals are first sorted into different categories and then further refined by a second classification process. This approach improves accuracy and efficiency in classifying underwater sounds into five different vessel categories: ships, boats, submarines, aircraft, and unknown sources.
To train the model, we used a private dataset of 300 audio recordings from underwater monitoring, carefully annotated with detailed information on the types and models of vessels present. We ensured that each category was represented in the testing set to evaluate the model’s performance. Through this process, we developed a deep neural network with two classification networks: one for initial sorting and another for fine-tuning the results.
The Cascade method effectively segments and expands the input data into different categories, allowing the model to prioritize binary classification and achieve higher overall precision. We tested our approach using various vessel types and found that it outperformed traditional machine learning algorithms in terms of accuracy. Our findings demonstrate the potential of the Cascade method for improving underwater acoustic signal classification and supporting maritime security applications.
To explain this concept in simpler terms, think of the Cascade method as a sorting system with multiple levels. First, it sorts signals into general categories based on their characteristics, like a rough preliminary sort. Then, it refines these categories by analyzing each signal more closely and making finer distinctions, like a final detailed sort. By using this approach, the model can identify vessels more accurately and efficiently, even in complex underwater environments with multiple sources of noise.
In summary, our article introduces the Cascade method for underwater acoustic signal classification, which leverages neural networks to improve accuracy and efficiency. By using a multi-level sorting system, this approach enables the model to prioritize binary classification and achieve higher overall precision. Our findings demonstrate the potential of the Cascade method for supporting maritime security applications and advancing the field of underwater acoustic signal processing.
Recognizing Underwater Acoustic Signals with Multilevel Cascading and Anonymization
