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arXiv 2019年

作者： Medvedev, Timur V. Pochinka, Olga V. Zinina, Svetlana Kh. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics 136 Rodionova Street Niznhy Novgorod Russia National Research University Higher School of Economics 25/12 Bolshaya Pecherckaya Street Niznhy Novgorod Russia Ogarev Mordovia State University 111v-2 Bolshevistskaya street Saransk Russia

We prove the existence of a continuous Morse energy function for an arbitrary topological flow with finite hyperbolic (in topological sense) chain recurrent set on a topological manifold of any dimension. This result is a partial solution of the Morse problem of existence of continuous Morse functions on any topological manifolds. Namely, we prove that a topological manifold admits a continuous Morse function if it admits a topological ow with finite hyperbolic chain recurrent set. Copyright © 2019, The Authors. All rights reserved.

关键词： Lyapunov functions

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Marine mammal calls detection in acoustic signals via gradient boosting model

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Proceedings of Meetings on Acoustics 2021年第1期44卷

作者： Mikhail Salin Alexander Ponomarenko 1Center of Hydroacoustics Institute of Applied Physics RAS Nizhny Novgorod 603950 RUSSIAN FEDERATION mikesalin@ipfran.ru 2Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Niznhy Novgorod RUSSIAN FEDERATION aponomarenko@hse.ru

This paper is devoted to methods of processing hydrophone records in order to identify specific signals, produced by marine mammals. The aim of processing is to detect a signal according to a certain pattern against the background of non-stationary noise. Development of a robust detection algorithm is of great interest because it could help to assess marine mammal occurrence and distribution at greater temporal and spatial scales by excluding laborious task of manually analyzing acoustic data. In addition, such algorithm may be implemented in automatic systems designed to warn vessel crew to avoid potential collision with whales. The data, studied in this paper, contained the calls of North Atlantic right whales, that were collected in the Gulf of St. Lawrence. Machine learning is exploited to solve a signal detection problem. The model was trained by a set of manually labelled signal fragments. Each fragment is labelled whether a whale call is present or not in the fragment. The proposed detection method is based on the two-level stacked gradient boosting models (XGBoost algorithm). The first-level model deals with spectral features, extracted from the short signal frames. The second model accumulates estimated output for a group of subsequent frames.

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