• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.5 s.110
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• pp.503-513
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• 2006

The duration time of aircraft noise event is also an important factor for the evaluation of civil aircraft noise, which is considered as a notable characteristic of military aircraft noise. SEL is proved as a suitable noise metric for the measuring military aircraft noise of various flight pattern considering the duration time of noise event. This study reviews whether SEL is a suitable for measuring civil aircraft noise and study shows SEL is fairly compensating the duration time of civil aircraft noise event for the evaluation of aircraft noise. Noise metric for the evaluation aircraft noise based on SEL is more appropriate than based on $L_{MAX}$ for compensating duration time of noise event either military aircraft or civil aircraft. In this reason, current formula of WECPNL based on energy average of measured $L_{MAX}$ for the evaluation of aircraft noise impact in 'Test Method of Noise and Vibration of Korea' is recommended to be amended to formula of WECPNL based on energy average of measured SEL considering compensation of noise event duration time, if WECPNL is not based on measured EPNL, a metric compensating duration time.

• Journal of the Korean Institute of Gas
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• v.27 no.1
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• pp.57-62
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• 2023

The Gas Regulator EVENT is a signal sent by the Remote Terminal Unit(RTU) installed in each local gas regulator (hereinafter referred to as "regulator"), and is an abnormal alarm that can be intuitively checked in our client server. This is an important data that enables immediate dispatch order and initial action in the situation room when a regolator abnormality occurs, and can analyze the cause of the regulator abnormality. Looking at the trend of EVENT data for regulator over the past three years, there is a clear trend of increasing unchecked EVENT data. The increase in non-checking event (actual abnormality or noise event) may mean that firstly, mechanical or pressure abnormality occurs in the actual regulator, and secondly, there is no abnormality in the actual regulator, but communication error occurred in the RTU, reset. EVENT Data may be formed as if an abnormality occurred in the static voltage due to an error, sensor error, power failure (instantaneous power failure), etc. Among them, this study analyzed the recently generated unchecked EVENT data to identify critical noise events among RTU errors, which are noise events, and reviewed ways to increase the reliability of Regulator EVENTs by reducing them.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.5 s.98
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• pp.550-557
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• 2005

Korean 'Law of Aviation' and 'Test Method of Measuring Noise and Vibration' designate to use WECPNL metric based on $L_{max}$ measurement for the accessment of aircraft noise in Korea. However, time duration of noise event can not be considered in $L_{max}$ metric in principle, compensation on the duration has been utilized. A study was done recently to evaluate appropriate duration compensation for the accessment of military aircraft noise under current metric of WECPNL and $L_{max}$. This study was carried out to evaluate what metric is the most appropriate to express aircraft noise including time duration of single noise event, computing and comparing noise exposure with 1 second noise measurement data of military aircraft in $L_{max}$, $L_{Aeq,\;T}$ and SEL. This study shows SEL is the most appropriate noise metric for the evaluation of noise exposure with time duration such as aircraft noise without compensation. It is suggested to use SEL noise metric instead of $L_{max}$ noise metric with duration compensation for the aircraft noise accessment either military aircraft or civilian aircraft.

• The Journal of the Acoustical Society of Korea
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• v.28 no.7
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• pp.674-679
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• 2009

This paper proposes a method of player event retrieval using combination of two functions: detection of player name in speech information and detection of sound event from audio information in golf videos. The system consists of indexing module and retrieval module. At the indexing time audio segmentation and noise reduction are applied to audio stream demultiplexed from the golf videos. The noise-reduced speech is then fed into speech recognizer, which outputs spoken descriptors. The player name and sound event are indexed by the spoken descriptors. At search time, text query is converted into phoneme sequences. The lists of each query term are retrieved through a description matcher to identify full and partial phrase hits. For the retrieval of the player name, this paper compares the results of word-based, phoneme-based, and hybrid approach.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1803-1810
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• 2018

Recently, many researches related to IoT (Internet of Things) have been actively conducted. In order to improve the context aware function of smart wearable devices using the IoT, we proposed a noise reduction method for the event data of the sensor part. In thisstudy, the adoption of the low - pass filter induces the attenuation of the abnormally measured value, and the benefit was obtained from the situation recognition using the event data of the sensor. As a result, we have validated attenuation for abnormal or excessive noise using event data detected and reported by 3-axis acceleration sensors on some devices, such as smartphones and smart watches. In addition, various pattern data necessary for real - time context aware were obtained through noise pattern analysis.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.115-122
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• 2007

The noises which occurs from the rolling stock can be divided largely into three classes and they are Rolling noise, Traction noise and Aerodynamic noise. In the event of braking the rolling stock which enter into the station, Brake shoes cause Fraction noise (braking noise) and excessive braking noise makes passengers and operators uncomfortable. This study is to reduce squeal noise and minimize displeasure by measuring the braking noise and defining the major noise sources and noise mechanism

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.914-916
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• 2016

The noise in a power signal degrades the detection rate of the power quality (PQ) event signals. We present a new wavelet de-noising technique for PQ event detection that employs the correlation-based thresholding instead of the wavelet-scale-based thresholding of existing schemes. The simulation results show that the proposed scheme is more robust to Gaussian and impulsive noisy conditions and has further improved detection ratio than existing schemes.

• ETRI Journal
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• v.39 no.6
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• pp.832-840
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• 2017

Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception-based spatial and spectral-domain noise-reduced harmonic features are extracted from multichannel audio and used as high-resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short-term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.