Bird Species Recognition Using Support Vector Machines

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Fagerlund, Seppo
dc.date.accessioned 2017-10-15T20:58:06Z
dc.date.available 2017-10-15T20:58:06Z
dc.date.issued 2007
dc.identifier.citation Fagerlund , S 2007 , ' Bird Species Recognition Using Support Vector Machines ' EURASIP Journal on Advances in Signal Processing , vol 2007 , 038637 , pp. 1-8 . DOI: 10.1155/2007/38637 en
dc.identifier.issn 1687-6180
dc.identifier.other PURE UUID: fb9d6843-f451-44e3-8e9e-29257d48a564
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/bird-species-recognition-using-support-vector-machines(fb9d6843-f451-44e3-8e9e-29257d48a564).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/14856995/38637.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/28357
dc.description.abstract Automatic identification of bird species by their vocalization is studied in this paper. Bird sounds are represented with two different parametric representations: (i) the mel-cepstrum parameters and (ii) a set of low-level signal parameters, both of which have been found useful for bird species recognition. Recognition is performed in a decision tree with support vector machine (SVM) classifiers at each node that perform classification between two species. Recognition is tested with two sets of bird species whose recognition has been previously tested with alternative methods. Recognition results with the proposed method suggest better or equal performance when compared to existing reference methods. en
dc.format.extent 1-8
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries EURASIP Journal on Advances in Signal Processing en
dc.relation.ispartofseries Volume 2007 en
dc.rights openAccess en
dc.title Bird Species Recognition Using Support Vector Machines en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Signal Processing and Acoustics en
dc.subject.keyword bird song
dc.subject.keyword feature extraction
dc.subject.keyword pattern recognition
dc.subject.keyword species recognition
dc.subject.keyword support
dc.subject.keyword vector machine (SVM)
dc.identifier.urn URN:NBN:fi:aalto-201710157217
dc.identifier.doi 10.1155/2007/38637
dc.type.version publishedVersion


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