The operation speed of the algorithm is the critical factor in the real-time monitoring of
infrasound signals. The existing methods mainly focus on how to improve the accuracy of …

A classification system is presented for discriminating different infrasound events using a
Parallel Neural Network Classifier Bank (PNNCB) consisting of Radial Basis Function (RBF) …

Discrimination between different rocket types is an important application for utilizing
infrasound in event monitoring within a range of 0-100 km. This is in contrast to traditional …

The main aim of this paper is to compare three feature extraction techniques, Discrete
Wavelet Transform, Time Scale Spectrum using Continuous Wavelet Transforms, and …

Infrasound is a longitudinal pressure wave [1-4]. The characteristics of these waves are
similar to audible acoustic waves but the frequency range is far below what the human ear …

This paper presents classification results for infrasonic events using practically all well-
known machine learning algorithms together with wavelet transforms for preprocessing. We …

A speaker verification system (SVS) is developed based on a bank of radial basis function
(RBF) neural modules (BRBFNM). The output thresholds of the RBF networks are set using …

The speed and accuracy of data classification are essential factors in the online monitoring
of infrasound waves. The available techniques for classifying these data are relatively high …

본 논문에서는 화산 분출 초저음파의 식별을 위해서 FSPNNC (Frequency Sub-bands Parallel
Neural NetworkClassification) 을 선택한다. FSPNNC 는 각기 다른 주파수 영역에서 …

The main aim of this paper is to compare three feature extraction techniques, Discrete
Wavelet Transform, Time Scale Spectrum using Continuous Wavelet Transforms, and …