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Advanced LIGO and Advanced Virgo ground-based interferometers are instrumentscapable to detect gravitational wave signals exploiting advanced laserinterferometry techniques. The underlying data analysis task consists inidentifying specific patterns in noisy timeseries, but it is made extremelycomplex by the incredibly small amplitude of the target signals. In thisscenario, the development of effective gravitational wave detection algorithmsis crucial. We propose a novel layered framework for real-time detection ofgravitational waves inspired by speech processing techniques and, in thepresent implementation, based on a state-of-the-art machine learning approachinvolving a hybridization of genetic programming and neural networks. The keyaspects of the newly proposed framework are: the well structured, layeredapproach, and the low computational complexity. The paper describes the basicconcepts of the framework and the derivation of the first three layers. Even ifthe layers are based on models derived using a machine learning approach, theproposed layered structure has a universal nature. Compared to more complexapproaches, such as convolutional neural networks, which comprise a parameterset of several tens of MB and were tested exclusively for fixed length datasamples, our framework has lower accuracy (e.g., it identifies 45% of lowsignal-to-noise-ration gravitational wave signals, against 65% of thestate-of-the-art, at a false alarm probability of $10^{-2}$), but has a muchlower computational complexity and a higher degree of modularity. Furthermore,the exploitation of short-term features makes the results of the new frameworkvirtually independent against time-position of gravitational wave signals,simplifying its future exploitation in real-time multi-layer pipelines forgravitational-wave detection with new generation interferometers.

A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals