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


Vol. 49,  No. 12, pp. 1702-1704, Dec.  2024
10.7840/kics.2024.49.12.1702


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  Abstract

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 estimated GMM and the Gaussian mixture model obtained from the input signal. If this distance exceeds a predefined threshold, the signal is detected as an unlearned novelty signal, indicating that it deviates from the training data distribution. This framework offers a new approach to identifying acoustic anomalies by leveraging model uncertainty and the probabilistic modeling capabilities of GMMs.

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[IEEE Style]

N. Kim, C. Chun, H. K. Kim, "Acoustic Novelty Detection Using Monte Carlo Dropout and Gaussian Mixture Model in Underwater Acoustic Environments," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 12, pp. 1702-1704, 2024. DOI: 10.7840/kics.2024.49.12.1702.

[ACM Style]

Nayeon Kim, Chanjun Chun, and Hong Kook Kim. 2024. Acoustic Novelty Detection Using Monte Carlo Dropout and Gaussian Mixture Model in Underwater Acoustic Environments. The Journal of Korean Institute of Communications and Information Sciences, 49, 12, (2024), 1702-1704. DOI: 10.7840/kics.2024.49.12.1702.

[KICS Style]

Nayeon Kim, Chanjun Chun, Hong Kook Kim, "Acoustic Novelty Detection Using Monte Carlo Dropout and Gaussian Mixture Model in Underwater Acoustic Environments," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 12, pp. 1702-1704, 12. 2024. (https://doi.org/10.7840/kics.2024.49.12.1702)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880