Automatic forest wood logging identification based on acoustic monitoring

Iosif Mporas; Michael Paraskevas

doi:10.1145/2903220.2903258

Automatic forest wood logging identification based on acoustic monitoring

Iosif Mporas, Michael Paraskevas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we describe a scheme for automatic identification of wood logging activity in forest based on acoustic surveillance. Specifically, we evaluate five machine learning classification algorithms using several audio descriptors for the identification of chainsaw wood logging sounds in the noisy environment of a forest. Different environmental noise interference levels, in terms of sound-to-noise ratio, were considered and the best performance was achieved using support vector machines.

Original language	English
Title of host publication	9th Hellenic Conference on Artificial Intelligence, SETN 2016
Editors	Antonis Bikakis, Dimitrios Vrakas, Nick Bassiliades, Ioannis Vlahavas, George Vouros
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450337342
DOIs	https://doi.org/10.1145/2903220.2903258
Publication status	Published - 18 May 2016
Event	9th Hellenic Conference on Artificial Intelligence, SETN 2016 - Thessaloniki, Greece Duration: 18 May 2016 → 20 May 2016

Publication series

Name	ACM International Conference Proceeding Series
Volume	18-20-May-2016

Conference

Conference	9th Hellenic Conference on Artificial Intelligence, SETN 2016
Country/Territory	Greece
City	Thessaloniki
Period	18/05/16 → 20/05/16

Keywords

Audio based surveillance
Audio processing
Biodiversity monitoring
Classification

Access to Document

10.1145/2903220.2903258

Cite this

Mporas, I., & Paraskevas, M. (2016). Automatic forest wood logging identification based on acoustic monitoring. In A. Bikakis, D. Vrakas, N. Bassiliades, I. Vlahavas, & G. Vouros (Eds.), 9th Hellenic Conference on Artificial Intelligence, SETN 2016 [a35] (ACM International Conference Proceeding Series; Vol. 18-20-May-2016). Association for Computing Machinery. https://doi.org/10.1145/2903220.2903258

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title = "Automatic forest wood logging identification based on acoustic monitoring",

abstract = "In this paper we describe a scheme for automatic identification of wood logging activity in forest based on acoustic surveillance. Specifically, we evaluate five machine learning classification algorithms using several audio descriptors for the identification of chainsaw wood logging sounds in the noisy environment of a forest. Different environmental noise interference levels, in terms of sound-to-noise ratio, were considered and the best performance was achieved using support vector machines.",

keywords = "Audio based surveillance, Audio processing, Biodiversity monitoring, Classification",

author = "Iosif Mporas and Michael Paraskevas",

year = "2016",

month = may,

day = "18",

doi = "10.1145/2903220.2903258",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

editor = "Antonis Bikakis and Dimitrios Vrakas and Nick Bassiliades and Ioannis Vlahavas and George Vouros",

booktitle = "9th Hellenic Conference on Artificial Intelligence, SETN 2016",

note = "9th Hellenic Conference on Artificial Intelligence, SETN 2016 ; Conference date: 18-05-2016 Through 20-05-2016",

}

Mporas, I & Paraskevas, M 2016, Automatic forest wood logging identification based on acoustic monitoring. in A Bikakis, D Vrakas, N Bassiliades, I Vlahavas & G Vouros (eds), 9th Hellenic Conference on Artificial Intelligence, SETN 2016., a35, ACM International Conference Proceeding Series, vol. 18-20-May-2016, Association for Computing Machinery, 9th Hellenic Conference on Artificial Intelligence, SETN 2016, Thessaloniki, Greece, 18/05/16. https://doi.org/10.1145/2903220.2903258

Automatic forest wood logging identification based on acoustic monitoring. / Mporas, Iosif; Paraskevas, Michael.

9th Hellenic Conference on Artificial Intelligence, SETN 2016. ed. / Antonis Bikakis; Dimitrios Vrakas; Nick Bassiliades; Ioannis Vlahavas; George Vouros. Association for Computing Machinery, 2016. a35 (ACM International Conference Proceeding Series; Vol. 18-20-May-2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Automatic forest wood logging identification based on acoustic monitoring

AU - Mporas, Iosif

AU - Paraskevas, Michael

PY - 2016/5/18

Y1 - 2016/5/18

N2 - In this paper we describe a scheme for automatic identification of wood logging activity in forest based on acoustic surveillance. Specifically, we evaluate five machine learning classification algorithms using several audio descriptors for the identification of chainsaw wood logging sounds in the noisy environment of a forest. Different environmental noise interference levels, in terms of sound-to-noise ratio, were considered and the best performance was achieved using support vector machines.

AB - In this paper we describe a scheme for automatic identification of wood logging activity in forest based on acoustic surveillance. Specifically, we evaluate five machine learning classification algorithms using several audio descriptors for the identification of chainsaw wood logging sounds in the noisy environment of a forest. Different environmental noise interference levels, in terms of sound-to-noise ratio, were considered and the best performance was achieved using support vector machines.

KW - Audio based surveillance

KW - Audio processing

KW - Biodiversity monitoring

KW - Classification

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DO - 10.1145/2903220.2903258

M3 - Conference contribution

AN - SCOPUS:84986571367

T3 - ACM International Conference Proceeding Series

BT - 9th Hellenic Conference on Artificial Intelligence, SETN 2016

A2 - Bikakis, Antonis

A2 - Vrakas, Dimitrios

A2 - Bassiliades, Nick

A2 - Vlahavas, Ioannis

A2 - Vouros, George

PB - Association for Computing Machinery

T2 - 9th Hellenic Conference on Artificial Intelligence, SETN 2016

Y2 - 18 May 2016 through 20 May 2016

ER -

Automatic forest wood logging identification based on acoustic monitoring

Abstract

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Conference

Keywords

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