Integrated Optimization of Long-Range Underwater Signal Detection, Feature Extraction, and Classification for Nuclear Treaty Monitoring

Abstract

We designed and jointly optimized an integrated signal processing chain for detection and classification of long-range passive-acoustic underwater signals recorded by the global geophysical monitoring network of the Comprehensive Nuclear-Test-Ban Treaty Organization. Starting at the level of raw waveform data, a processing chain of signal detection, feature extraction, and signal classification was designed and jointly optimized to the task. Relevant waveform segments were in a first step identified by a generic, flexibly parameterized detection algorithm on a long- to short-term averages' ratio of the spectral energy. For representation, general-purpose sound processing features, with an added focus on spectral and cepstral features, were extracted from the detected segments. As classifiers, support vector machines with different kernel functions were employed alongside other baseline learning algorithms. The free parameters of the overall toolchain (i.e., trigger algorithm parameters and classifier hyperparameters) were jointly optimized in a cross-validation setting, either according to the cross-validation classification error or the cross-validation area under the receiver operating characteristic curve. Experiments demonstrate that our method outperforms machine learning algorithms task-tailored to a previous, human-expert-designed preprocessing chain. The presented approach can be adapted to a wide range of problems that can benefit from jointly optimizing parameters of preprocessing and classification algorithm.