Sound Classification Performance of Deep Neural Networks in the Presence of Disturbing Sounds (딥러닝을 이용한 소리 분류 시 방해음의 영향 분석)

Wongeun Oh (오원근); Lim, Dong Kyun (임동균)

doi:10.34163/jkits.2020.15.6.006

Sound Classification Performance of Deep Neural Networks in the Presence of Disturbing Sounds

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2020, 15(6), pp.973-981
DOI : 10.34163/jkits.2020.15.6.006
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Received : November 27, 2020
Accepted : December 11, 2020
Published : December 31, 2020

Wongeun Oh ¹, Lim, Dong Kyun ²

¹순천대학교
²한양사이버대학교

Accredited

ABSTRACT

Environmental sound classification is an area that automatically classifies sounds in our surroundings. It can be applied to home automation, security, and surveillance. Recently, the deep learning approaches have been adopted as a classifier for increasing performance. In this method, a deep neural network is trained using many sound data, and after the learning is completed, the microphone pickup sound is applied for classification. However, during this stage, the ambient noise can be put into the microphone along with the sound to be identified. And the sound cannot be properly classified due to this disturbing noise. The recognition rate of the deep neural network decreases as the loudness of the disturbing sound increases, but the analysis about the noise effect on the classification have been limited. In this paper, we present the effect of the disturbing noise on the classification rate. For this purpose, UrbanSound8K, which is composed of 10 types of urban environmental sounds, is used for training and test data. And the VGG16-based CNN which shows good performance in image classification was adopted as a baseline model. For the disturbing noise, we use three types of sounds that are consist of daily noises (hairdryer, vacuum cleaner, faucet water, and hammer), voices(male, female, and synthesized sound), and music(cello, piano, and trumpet). In the experiment, these disturbing noises are mixed with the clean sounds so that the signal-to-noise ratio is in the range of -50dB to 50dB. Then the mixed sound was applied to a deep neural network to obtain a relative recognition rate when compared to the clean cases. The results show that the recognition rate is more than 90% compared to the clean sound cases when the SNR is between 10 and 15 dB, and 95% or more when the SNR is greater than 20 dB, regardless of the type of the disturbing sound.

KEYWORDS

Environmental sound classification, Noise, CNN, VGG16, UrbanSound8K, Signal to noise ratio

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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

Sound Classification Performance of Deep Neural Networks in the Presence of Disturbing Sounds

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* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

Sound Classification Performance of Deep Neural Networks in the Presence of Disturbing Sounds

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