Acoustic Novelty Detection Using Monte Carlo Dropout and Gaussian Mixture Model in Underwater Acoustic Environments

In this paper, we propose a method for detecting novel acoustic signals that deviate from the learned data distribution in underwater acoustic environments. Specifically, we utilize Monte Carlo dropout (MCDO) to quantify the uncertainty in the model’s predictions and model the distribution of predictions for normal signals using a Gaussian Mixture Model (GMM). We measure the distance between the e<span style=color:#999999 > ... </span><a href=javascript:; onclick=onClickReadNode('NODE12003207');fn_statistics('Z354','null','null'); style='color:#999999;font-size:14px;text-decoration:underline;' >전체 초록 보기</a>