The CLEAR 2006 CMU Acoustic Environment Classification System

Malkin, Robert

doi:10.1007/978-3-540-69568-4_30

Cited by 1 publication

(1 citation statement)

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“…CHiME dealt with keyword recognition in noisy environments [11]; keyword recognition performance in various noisy environments was comparatively evaluated, with the result that the measured performance of human recognition and that of the classifier with the best performance showed a difference in recognition rate of about 5%. MIREX assigns tasks related to music information retrieval (MIR) such as music beat tracking, chord estimation, melody extraction, and genre classification [12]. The CLEAR assignment evaluates systems that recognize human behavior, reactions, and information from the surrounding environment using multimodal video/audio information.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network based Audio Event Classification

Lim¹,

Lee²,

Park³

et al. 2018

KSII TIIS

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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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Section: Related Workmentioning

confidence: 99%