A Clustering Approach for the Detection of Acoustic/Seismic Signals of Unknown Structure

Roee Diamant, Dror Kipnis, Michele Zorzi

Research output: Contribution to journalArticlepeer-review


We focus on the detection of sporadic low-power acoustic/seismic signals of unknown structure and statistics, such as the detection of sound produced by marine mammals, low-power underground signals, or the discovery of events such as volcano eruptions. In these cases, since the ambient noise may be fast time varying and may include many noise transients, threshold-based detection may lead to a significant false alarm rate. Instead, we propose a detection scheme that avoids the use of a decision threshold. Our method is based on clustering the samples of the observed buffer according to a binary hidden Markov model to discriminate between 'noise' and 'signal' states. Our detector is a modification of the Baum-Welch algorithm that takes into account the expected continuity of the desired signal and obtains a robust detection using the complex but flexible general Gaussian mixture model. The result is a combination of a constrained expectation-maximization algorithm with the Viterbi algorithm. We evaluate the performance of our scheme in numerical simulations, in a seimic test, and in an ocean experiment. The results are close to the hybrid Cramér-Rao lower bound and show that, at the cost of some additional complexity, our proposed algorithm outperforms common benchmark methods in terms of detection and false alarm rates, and also achieves a better accuracy of the time of detection. To allow reproducibility of the results, we publish our code.

Original languageEnglish
Article number8077756
Pages (from-to)1017-1029
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number2
StatePublished - Feb 2018

Bibliographical note

Funding Information:
Manuscript received February 15, 2017; revised August 8, 2017; accepted September 25, 2017. Date of publication October 20, 2017; date of current version January 26, 2018. This work was supported in part by the NATO Science for Peace and Security Programme under Grant G5293. Part of this work has been presented at the 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Edinburgh, U.K., July 2016. (Corresponding author: Roee Diamant.) R. Diamant and D. Kipnis are with the Department of Marine Technology, University of Haifa, 3498838 Haifa, Israel (e-mail: roeed@univ.haifa.ac.il; dkipnis@campus.haifa.ac.il).

Publisher Copyright:
© 1980-2012 IEEE.


  • Acoustic detection
  • Baum-Welch algorithm
  • Viterbi algorithm (VA)
  • clustering
  • detection in low signal-to-noise ratio (SNR)
  • expectation-maximization (EM)
  • seismic detection
  • time-of-arrival (ToA) estimation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences (all)


Dive into the research topics of 'A Clustering Approach for the Detection of Acoustic/Seismic Signals of Unknown Structure'. Together they form a unique fingerprint.

Cite this