Thesis defense: Marielle Malfante

on the October 3, 2018

October 3, 2018 at 10:15 am
Marielle Malfante from Gipsa-Lab will defend her thesis "Automatic classification in natural signals for environmental monitoring". Her thesis is directed by Jérôme Mars (co-director: Mauro Dalla Mura).
Automatic classification in natural signals for environmental monitoring


This manuscript summarizes a three years work addressing the use of machine learning for the automatic analysis of natural signals. The main goal of this PhD is to produce efficient and operative frameworks for the analysis of environmental signals, in order to gather knowledge and better understand the considered environment. Particularly, we focus on the automatic tasks of detection and classification of natural events.

This thesis proposes two tools based on supervised machine learning (Support Vector Machine, Random Forest) for (i) the automatic classification of events and (ii) the automatic detection and classification of events. The success of the proposed approaches lies in the feature space used to represent the signals. This relies on a detailed description of the raw acquisitions in various domains: temporal, spectral and cepstral. A comparison with features extracted using convolutional neural networks (deep learning) is also made, and favours the physical features to the use of deep learning methods to represent transient signals.

The proposed tools are tested and validated on real world acquisitions from different environments: (i) underwater and (ii) volcanic areas.
The first application considered in this thesis is devoted to the monitoring of coastal underwater areas using acoustic signals: continuous recordings are analysed to automatically detect and classify fish sounds. A day to day pattern in the fish behaviour is revealed.
The second application targets volcanoes monitoring: the proposed system classifies seismic events into categories, which can be associated to different phases of the internal activity of volcanoes.
The study is conducted on six years of volcano-seismic data recorded on Ubinas volcano (Peru). In particular, the outcomes of the proposed automatic classification system helped in the discovery of misclassifications in the manual annotation of the recordings. In addition, the proposed automatic classification framework of volcano-seismic signals has been deployed and tested in Indonesia for the monitoring of Mount Merapi. The software implementation of the framework developed in this thesis has been collected in the Automatic Analysis Architecture (AAA) package and is freely available.

Jury members

Mr Christian JUTTEN, Professor, Universite? Grenoble Alpes, GIPSA-LAB, President of the jury
Mr Stéphane CANU, Professor, INSA Rouen, LITIS, Rapporteur
Mr Ronan FABLET, Professorr, IMT Atlantique, LabSTICC, Rapporteur
Mrs Carole NAHUM, Head of the environment and geosciences scientific field DGA/DS/MRIS, Examiner
Mrs Eléonore STUTZMANN, Professor, Institut de Physique du Globe de Paris, Examiner
Mr Jérome MARS, Professor, Grenoble INP, GIPSA-LAB, Director of the thesis
Mr Mauro DALLA MURA, Researcher, Grenoble INP, GIPSA-LAB, Co-director of the thesis
Mrs Odile GERARD, DGA Techniques navales/SDT/SCN/LSM, Guesy
Mr Jean-Philippe METAXIAN, Researcher, IRD, Institut de Physique du Globe de Paris, Guest

Published on September 11, 2018

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