• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.845-853
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• 2020

In this paper, we propose a method for classifying environmental sound for selective noise cancellation in industrial sites. Noise in industrial sites causes hearing loss in workers, and researches on noise cancellation have been widely conducted. However, the conventional methods have a problem of blocking all sounds and cannot provide the optimal operation per noise type because of common cancellation method for all types of noise. In order to perform selective noise cancellation, therefore, we propose a method for environmental sound classification based on deep learning. The proposed method uses new sets of acoustic features consisting of temporal and statistical properties of Mel-spectrogram, which can overcome the limitation of Mel-spectrogram features, and uses convolutional neural network as a classifier. We apply the proposed method to five-class sound classification with three noise classes and two non-noise classes. We confirm that the proposed method provides improved classification accuracy by 6.6% point, compared with that using conventional Mel-spectrogram features.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.7-9
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• 2014

An auditory experiment was conducted to establish annoyance criteria for floor impact noise in apartment buildings. Heavyweight floor impact sounds were recorded using an impact ball; the impact sound pressure level (SPL) together with the temporal decay rate (DR), which is quantified by the dB drop per second, was analyzed. For the experiment, A-weighted exposure levels of the heavy-weight floor impact sounds ranging 34~73 dB were evaluated at 3 dB intervals. Participants used a 7-point verbal scale to evaluate the level of annoyance from floor impact noise. The results show that the annoyance increases with increasing impact SPL and decreasing DR. Consequently, a classification and an acceptable level of floor impact sounds were proposed.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.9
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• pp.682-686
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• 2014

Nowadays, the needs to revise the classification criteria for noise emission facilities have been suggested by the related industries. Because there existed many reasonable factors in the criteria regarding the noise emission facilities. And the noise emission facility classification criterion of the print machine changed from 50 HP to 100 HP in 2013. But the increasement of the noise emission facility classification criterion of the print machine can cause adverse effects like the bigger noise. So, in this paper, we measured the print machine's sound power level according to the changes of the print machine's power to assess the adverse effects. The measurement method applied with KS I ISO 9614-2(1996). The corelation between the sound power level and the power of print machines was analyzed by regression analysis. In this paper, we found that the sound power level of the print machines can increase about 1.3 dB in the condition of that the power of print machine increases from 50 HP to 100 HP. And we found that the sound power level of the print machines can increase about 1.0 dB for a increasement of 1,000 SPH(sheet per hour) of printing speed. The noise emission characteristics of print machine stuied in this paper will be useful to design the noise reduction plan in the future.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.469-474
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• 2018

As urban population increases, research on urban environmental noise is getting more attention. In this study, we classify the abnormal noise occurring in traffic situation by using a deep learning algorithm which shows high performance in recent environmental noise classification studies. Specifically, we classify the four classes of tire skidding sounds, car crash sounds, car horn sounds, and normal sounds using convolutional neural networks. In addition, we add three environmental noises, including rain, wind and crowd noises, to our training data so that the classification model is more robust in real traffic situation with environmental noises. Experimental results show that the proposed traffic sound classification model achieves better performance than the existing algorithms, particularly under harsh conditions with environmental noises.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.1142-1146
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• 2007

In this studies, subjective evaluation of heavy-weight floor impact sound through classification was conducted. Heavyweight impact sounds generated by an impact ball were recorded through dummy heads in apartment buildings. The recordings were classified according to the frequency characteristics of the floor impact sounds which are influenced by the floor structure with different boundary conditions and composite materials. The characteristics of the floor impact noise were investigated by paired comparison tests and semantic differential tests. Sound sources for auditory experiment were selected based on the actual noise levels with perceptual level differences. The results showed that roughness and fluctuation strength as well as loudness of the heavy-weight impact noise had a major effect on annoyance.

• KIEAE Journal
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• v.4 no.3
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• pp.37-44
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• 2004

In this study, foreign and domestic noise criteria on walls, floors, and water supply facilities were reviewed and the results are as follows : regulation can be divided two things, one is on thickness the other is sound insulation performance. Green Building regulation based on the law and this have sound classification systems. Since these regulations are not established in Korea. The noise regulation on water supply-drain facilities and domestic guideline on interior noise level is needed. Foreign regulations are stricter than those of ours. And those has sound classification system for the better acoustic condition of inhabitants'.

• Speech Sciences
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• v.4 no.2
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• pp.41-54
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• 1998

Speech signals are, depending on the characteristics of waveform, classified as voiced sound, unvoiced sound, and silence. Voiced sound, produced by an air flow generated by the vibration of the vocal cords, is quasi-periodic, while unvoiced sound, produced by a turbulent air flow passed through some constriction in the vocal tract, is noise-like. Silence represents the ambient noise signal during the absence of speech. The need for deciding whether a given segment of a speech waveform should be classified as voiced, unvoiced, or silence has arisen in many speech analysis systems. In this paper, a voiced/unvoiced/silence classification algorithm using spectral change in the wavelet transformed signal is proposed and then, experimental results are demonstrated with our discussions.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.712-717
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• 2004

Recently, the concern for the environmental noise has increased due to the growing of the living standard. The environmental noise regulations based on the equivalent noise level are widely used. However, the noise level, which Is based mainly on the magnitude with A-weighting, the important characteristics of noises in frequency and time domains and the impulsive nature cannot be assessed properly. These can have substantial effects on how human respond to noise. Therefore, the noise evaluation methodology based on the sound quality rather than the equivalent noise level can be more suitable to represent human response to the environmental noise. This paper describes the study on environmental noise quality analysis for various noises. A cluster analysis was carried out and the noises were classified into several clusters using the values of sound quality metrics. The classification was confirmed by comparing time and frequency characteristics of the noises. And then the result of Jury testing was analysis.

• Proceedings of the IEEK Conference
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• summer
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• 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.