Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Republic of Korea.
Sonar System PMO, Agency for Defense Development, Changwon 51618, Republic of Korea.
Sensors (Basel). 2022 Dec 20;23(1):28. doi: 10.3390/s23010028.
The importance of active sonar is increasing due to the quieting of submarines and the increase in maritime traffic. However, the multipath propagation of sound waves and the low signal-to-noise ratio due to multiple clutter make it difficult to detect, track, and identify underwater targets using active sonar. To solve this problem, machine learning and deep learning techniques that have recently been in the spotlight are being applied, but these techniques require a large amount of data. In order to supplement insufficient active sonar data, methods based on mathematical modeling are primarily utilized. However, mathematical modeling-based methods have limitations in accurately simulating complicated underwater phenomena. Therefore, an artificial intelligence-based sonar signal synthesis technique is proposed in this paper. The proposed method modified the major modules of the Tacotron model, which is widely used in the field of speech synthesis, in order to apply the Tacotron model to the field of sonar signal synthesis. To prove the validity of the proposed method, spectrograms of synthesized sonar signals are analyzed and the mean opinion score was measured. Through the evaluation, we confirmed that the proposed method can synthesize active sonar data similar to the trained one.
由于潜艇的静音化和海上交通的增加，主动声纳的重要性日益增加。然而，由于多路径的声波传播和由于多个杂波引起的低信噪比，使用主动声纳进行水下目标的检测、跟踪和识别变得非常困难。为了解决这个问题，最近备受关注的机器学习和深度学习技术正在被应用，但是这些技术需要大量的数据。为了补充不足的主动声纳数据，主要利用基于数学建模的方法。然而，基于数学建模的方法在准确模拟复杂的水下现象方面存在局限性。因此，本文提出了一种基于人工智能的声纳信号合成技术。所提出的方法修改了 Tacotron 模型的主要模块，该模型在语音合成领域得到了广泛应用，以便将 Tacotron 模型应用于声纳信号合成领域。为了验证所提出方法的有效性，对合成声纳信号的频谱图进行了分析，并测量了平均意见分。通过评估，我们确认了所提出的方法可以合成与训练数据相似的主动声纳数据。
