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Statements

Subject Item
n2:RIV%2F62156489%3A43110%2F13%3A00204936%21RIV14-MSM-43110___
rdf:type
skos:Concept n12:Vysledek
rdfs:seeAlso
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6613984
dcterms:description
This paper describes classification of sound recordings based on their audio features. This is useful for querying large datasets, searching for recordings with some desired content. We use musical recordings as well as birdsongs recordings, which usually have rich structure and contain a lot of patterns suitable for classification. We present two different classification methods, one for musical recordings and one for birdsongs. These methods are compared and their differences are discussed. We use feature vectors that capture the audio content of recording as a whole piece and then classify these feature vectors using combination of the Self-organizing map and the Learning Vector Quantization, which represent a powerful algorithm using unlabeled as well as labeled data. In case of birdsongs we use feature vectors representing time frames of a recording. This paper describes classification of sound recordings based on their audio features. This is useful for querying large datasets, searching for recordings with some desired content. We use musical recordings as well as birdsongs recordings, which usually have rich structure and contain a lot of patterns suitable for classification. We present two different classification methods, one for musical recordings and one for birdsongs. These methods are compared and their differences are discussed. We use feature vectors that capture the audio content of recording as a whole piece and then classify these feature vectors using combination of the Self-organizing map and the Learning Vector Quantization, which represent a powerful algorithm using unlabeled as well as labeled data. In case of birdsongs we use feature vectors representing time frames of a recording.
dcterms:title
Audio data classification by means of new algorithms Audio data classification by means of new algorithms
skos:prefLabel
Audio data classification by means of new algorithms Audio data classification by means of new algorithms
skos:notation
RIV/62156489:43110/13:00204936!RIV14-MSM-43110___
n12:predkladatel
n17:orjk%3A43110
n3:aktivita
n4:Z
n3:aktivity
Z(MSM6215648904)
n3:dodaniDat
n7:2014
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n16:5032407 n16:1250019
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n22:D
n3:duvernostUdaju
n10:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
62500
n3:idVysledku
RIV/62156489:43110/13:00204936
n3:jazykVysledku
n20:eng
n3:klicovaSlova
sound processing; LVQ; SOM; HMM; semi-supervised learning; classification
n3:klicoveSlovo
n6:sound%20processing n6:semi-supervised%20learning n6:classification n6:SOM n6:HMM n6:LVQ
n3:kontrolniKodProRIV
[A4757E260B38]
n3:mistoKonaniAkce
Rome, Italy
n3:mistoVydani
Rome, Italy
n3:nazevZdroje
Proceedings of the 36 th International Conference on Telecommunikations and Signal Processing
n3:obor
n9:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n7:2013
n3:tvurceVysledku
Škorpil, Vladislav Fejfar, Jiří Šťastný, Jiří
n3:typAkce
n18:WRD
n3:zahajeniAkce
2013-07-02+02:00
n3:zamer
n23:MSM6215648904
s:numberOfPages
5
n19:doi
10.1109/TSP.2013.6613984
n21:hasPublisher
TSP
n11:isbn
978-1-4799-0404-4
n15:organizacniJednotka
43110