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 | |
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