TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Convolutional Neural Network based Audio Event Classification |
| 영문제목(English Title) |
Convolutional Neural Network based Audio Event Classification |
| 저자(Author) |
Minkyu Lim
Donghyun Lee
Hosung Park
Yoseb Kang
Junseok Oh
Jeong-Sik Park
Gil-Jin Jang
Ji-Hwan Kim
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| 원문수록처(Citation) |
VOL 12 NO. 06 PP. 2748 ~ 2760 (2018. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
This paper proposes an audio event classification method based on convolutional neural networks (CNNs). CNN has great advantages of distinguishing complex shapes of image. Proposed system uses the features of audio sound as an input image of CNN. Mel scale filter bank features are extracted from each frame, then the features are concatenated over 40 consecutive frames and as a result, the concatenated frames are regarded as an input image. The output layer of CNN generates probabilities of audio event (e.g. dogs bark, siren, forest). The event probabilities for all images in an audio segment are accumulated, then the audio event having the highest accumulated probability is determined to be the classification result. This proposed method classified thirty audio events with the accuracy of 81.5% for the UrbanSound8K, BBC Sound FX, DCASE2016, and FREESOUND dataset.
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| 키워드(Keyword) |
Audio event classification
Convolutional neural networks
Deep learning
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| 파일첨부 |
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