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Analysis of ecoacoustic recordings, session at International Conference

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Subject: Analysis of ecoacoustic recordings, session at International Conference on Ecological Informatics, Germany, September 24-28
From: Jérôme SUEUR <>
Date: Thu, 1 Feb 2018 14:59:54 +0000
Dear colleagues,

We are pleased to announce a session entitled "Analysis of ecoacoustic recordings: detection, segmentation and classification" at the next International Conference on Ecological Informatics to be held in Jena,
 Germany, September 24-28. All information about conference organisation is available at

This session mainly aims at sharing technical developments in sound analysis for ecoacoustic research (see abstract below).

We are very keen on reading your abstracts that should be submitted before March, 18 at

Do no hesitate to contact us if you have any query.

We look forward to meeting you in Jena,

Best regards,

Jérôme Sueur(1) and Dan Stowell(2)

1 - Muséum national d'Histoire naturelle, France
2 - Queen Mary University of London, UK

Abstract - Ecoacoustics is a newly emerged discipline that aims at tackling ecological research questions through the lens of sound analysis. Ecoacoustics covers several questions in marine, freshwater and terrestrial environments dealing with biodiversity monitoring, population ecology, community ecology and landscape ecology. One of the key approaches of ecoacoustics consists in identifying sounds of ecological importance in environmental recordings that were collected in an unattended way by automatic recorders. This search task is made difficult by the occurrence of background noise due to human activities, the co-occurrence of several sounds of interest, the degradation of the sounds of interest related to their propagation in the environment, a high-degree of variability of the sounds of interest, a large amount of data, and a lack of reference archives. Solutions including computer processes are currently in development to try to get around these difficulties. This session will be the occasion to report and share new techniques involving signal analysis, machine learning, deep learning and high dimension statistics for advances in detection, segmentation, supervised and unsupervised classification of sound events.

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