CREATING A DATASET OF ACOUSTIC SIGNALS OF THE WATER ENVIRONMENT FOR TRAINING A NEURAL NETWORK FOR NOISE SUPPRESSION

Abstract

The analysis of acoustic signals in the water environment is a complex task, complicated by the small number of available datasets and Neural networks are a relevant and powerful tool for classifying acoustic signals in the water environment. Taking into account the current problems in this area, it is advisable to create a neural network framework that can work with acoustic noise, in which the target signal is noisy with background noise of the water environment, which corresponds to real conditions. To solve this problem, a framework of several neural networks can be used, which as a result perform the tasks of noise suppression and subsequent classification. The ability to suppress background noise will improve classification accuracy by filtering out spectral components that are not typical of watercraft. The presence of artifacts uncharacteristic of the target object complicates the classification process, as unnecessary characteristics of the water environment lead to the neural network learning patterns that are not typical for watercraft and reduce classification accuracy. Testing a neural network for noise suppression requires a dataset with a sufficient signal-to-noise ratio that corresponds to real water environment signals. Also, training such neural networks often requires sets of pairs of clean and noisy samples, where the neural network will suppress noise from the noisy samples, and have examples of clean samples as a reference for comparing the work done. The process of obtaining water environment datasets by recording real noises is a rather costly and complex process that does not guarantee satisfactory results. Therefore, the task of creating water environment noise with a given signal-to-noise ratio and the presence of specific vessel noise and background noise in the required specified ratio is relevant. The aim of this work is to create a dataset for training a neural network to suppress background noise in the aquatic environment. Methodology. The software for creating the dataset and the neural network code were developed using Python in the Microsoft Visual Studio Code environment. Scientific novelty. The approach to creating an aquatic environment dataset from two datasets was improved, and a direction for further work to achieve better results was proposed. Conclusions. The proposed approach to dataset creation showed a lower signal-to-noise ratio compared to the approach described in the article. Future plans for the development and improvement of the dataset are described.