• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.273-277
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• 2007

In case of the Hydraulic Turbine Dynamo operating for Waterpower Generation, it makes very huge and loud noises, and it influences bad effect physically as same as mentally to those people who are working inside of power plant, and brings the decline of an effective working efficiency. However, its evaluation method or measure about such noise reflects merely its physical attribute which is sensuous Loudness of the Noise itself, since the accumulation effect of Noise or the meaning connected with psychological response did not reflect, it is the actual state that a rational evaluation is unable to expect. Consequently, this Study has attempted to evaluate the Noise of Hydraulic Turbine Dynamo by analyzing the sound quality using Zwicker‘s Psychological Acoustic Parameter, after classification by its positions of the Noise occurring at Hydraulic Turbine Dynamo.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• MALSORI
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• no.33_34
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• pp.11-22
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• 1997

The degrees of aspiration in Korean [ㅅ] and [ㅆ] were measured in terms of VOT. The measurements were compared to the aspiration in Korean stops and affricates. It was shown that [ㅅ] should be classified as an 'aspirated' sound with Korean aspirated stops and affricates [$p^h, {\;}t^h, {\;}k^h, {\;}t{\int}$], contrary to the traditional classification of the sound as unaspirated. [ㅆ] was confirmed to be in the same group as other Korean 'tense' sounds. It was pointed out that there was a gap in the typology of Korean consonants. The gap was created by the lack of the unaspirated counterpart of [ㅅ]. It was suggested that an extinct Korean sound [$\triangle$] be considered as a possible candidate for the gap. Also a perception test was suggested for the further acoustical analysis of Korean [ㅅ] and [ㅆ].

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.763-770
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• 2004

The important issue of emotion recognition from speech is a feature extracting and pattern classification. Features should involve essential information for classifying the emotions. Feature selection is needed to decompose the components of speech and analyze the relation between features and emotions. Specially, a pitch of speech components includes much information for emotion. Accordingly, this paper searches the relation of emotion to features such as the sound loudness, pitch, etc. and classifies the emotions by using the statistic of the collecting data. This paper deals with the method of recognizing emotion from the sound. The most important emotional component of sound is a tone. Also, the inference ability of a brain takes part in the emotion recognition. This paper finds empirically the emotional components from the speech and experiment on the emotion recognition. This paper also proposes the recognition method using these emotional components and the transition probability.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.707-711
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• 2004

Recently, the interests about noises have increased with the rapid development of our living environment Until now the estimation methods to sounds have used the equivalent levels. The sensitivities of human beings aren't considered in these methods. It's a situation to need new estimation methods for environmental noises. They must be analyzed by the characteristics of sounds before making the noise regulations newly. In this study, the noises were measured around our living environment And the frequency analysis, Sound Quality Metrics, the cluster analysis and so on are used to classify the environmental noises.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.82-88
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• 2003

The nondestructive internal quality evaluation of agricultural products has been strongly required from the needs for individual inspection. Recently, the ultrasonic wave has been considered as a solution fur this problem, and an ultrasonic system was constructed for the ultrasonic NDE of fruits and vegetables in our previous work. In this paper, the practical applicability of our ultrasonic system is tested fur the inspection of internal defects (central cavity) in Atlantic potato. Sound speed and RMS of transmitted ultrasonic wave signal were measured and classification algorithm using 2 dimensional stochastic analysis. was presented. Experimental results showed greater value of sound speed and RMS (root mean square) of transmitted signal in normal samples than in abnormal samples with cavity. Also a stochastic method to distinguish normal and abnormal showed fault detection rate less than 5%.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.6
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• pp.200-207
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• 2001

In this paper, a new method for Partial field decomposition is developed that is based on the beamforming algorithm for the application of acoustical holography to a composite sound field generated by multiple incoherent sound sources. In the proposed method, source Positions are first predicted by MUSIC(multiple signal classification) algorithm. The composite sound fields can then be decomposed into each partial field by the beamforming. Results of both numerical simulations and experiments show that the method can find each partial field very accurately and effectively, and that it also has Potential to be used for application to distributed sources.

• 중국학논총
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• no.64
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• pp.53-73
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• 2019

In the teaching of Chinese characters, making full use of the phonetic function of phonetic symbols can help learners improve their learning efficiency. The research on the characteristics of phonetic symbols and the rules of their construction is the premise of teaching Chinese characters with phonetic symbols. The phonetic symbols that can accurately prompt the pronunciation of the whole word and the homophone characters that they constitute provide the applicable materials for the teaching of Chinese characters. The split method simply and intuitively reflects the internal relationship among shape, sound and meaning in pictophonetic characters. "The analogy method of homophonic character group" and "the converse method of homophonic character group" are the combination of the function of the sound prompt and the characteristics of the analogy and induction of homophonic character, which can not only help students save the time of memorizing the sound, but also effectively increase the amount of literacy. The quantitative analysis of phonetic symbols and homophone symbols is of great significance to the classification of Chinese characters and the improvement of textbook editing.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.42-48
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• 2016

Since a variety of sound occur in same place and similar sound occurs in other places, the performance of acoustic scene classification is not guaranteed in case of insufficient training data. A Bag of Words (BOW) based histogram feature is foreseen as a method to overcome the problem. However, since the histogram features is made by using a feature distribution, the ordering of sequence of features is ignored. A temporal information such as periodicity and stationarity are also important for acoustic scene classification. In this paper, temporal features about a periodicity and a stationarity are extracted by using a recurrent quantification analysis. In the experiment, performance of the proposed method is shown better than other baseline methods.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.406-413
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• 2020

In this paper, to improve the classification accuracy of bird and amphibian acoustic sound, we utilize GLU (Gated Linear Unit) and Self-attention that encourages the network to extract important features from data and discriminate relevant important frames from all the input sequences for further performance improvement. To utilize acoustic data, we convert 1-D acoustic data to a log-Mel spectrogram. Subsequently, undesirable component such as background noise in the log-Mel spectrogram is reduced by GLU. Then, we employ the proposed temporal self-attention to improve classification accuracy. The data consist of 6-species of birds, 8-species of amphibians including endangered species in the natural environment. As a result, our proposed method is shown to achieve an accuracy of 91 % with bird data and 93 % with amphibian data. Overall, an improvement of about 6 % ~ 7 % accuracy in performance is achieved compared to the existing algorithms.