• 한국음향학회지
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• 제43권1호
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• pp.72-77
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• 2024

최근 여러 산업 분야에서 음원 위치 추정의 필요성이 커지고 있다. 기존 음원 위치 추정 방법들 중에서, 음향인텐시티 계측법은 작은 마이크로폰 어레이에서도 높은 정확도를 가지는 장점이 있다. 그러나, 높은 헬름홀츠 수에서의 위치 추정 오차 증가는 이 방법의 한계로 지적되어 왔다. 본 연구에서는 이러한 헬름홀츠 수에 따른 인텐시티 편향 오차를 딥러닝을 통해 보상하는 방법을 제안한다. 제안된 방법은 정사면체 마이크로폰 어레이에서 헬름홀츠 수에 대해 측정된 음향인텐시티 벡터를 입력했을 때, 보상된 음향 인텐시티 벡터를 도출하는 밀집 층 기반의 딥러닝 모델을 적용하여 정확한 음원 위치의 추정을 가능케 한다. 본 연구의 제안 모델은, 0.1 < kd < 3.0의 모든 음원 방향에 대한 시뮬레이션 데이터를 기반으로 검증하였다. 이를 통해, 딥러닝 기반 접근 방식은 음향 인텐시티 벡터 기반의 음원 추정법을 적용하는데 있어서 측정 주파수 범위를 확장하고 다양한 크기를 갖는 마이크로폰 어레이에 적용할 수 있음을 확인하였다.

• Educational Technology International
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• 제14권1호
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• pp.41-53
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• 2013

On the basis of two premises and embodied cognition theory, the vibration tactile learning is proposed as an effective method for foreign language learning. The premises are: the real nature of language is sound and the source of sound is vibration. According to embodied cognition theory, cognition is inherently connected to bodily sensation rather than metaphysical and independent. As a result, the vibration tactile learning is: people are able to learn foreign language better by listening to sound and experiencing its vibration through touch rather than solely listening to sound. The effectiveness of vibration tactile learning is tested with two instructional media theories: media comparison and media attribute. For the comparison, an experiment is conducted with control and experimental groups. The attributes of vibration tactile media are investigated in points of relationships with the learning process. The experiment results indicate a small effect on the increased mean score. Three kinds of relationships are found between the media attribute and learning process: enforced stimulus, facilitated pronunciation, and assimilation of resonance to sound patterns through touch. Finally, this paper proposes a new theoretical development for instructional media research: an embodied cognition based media research and development.

• 스마트미디어저널
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• 제11권1호
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• pp.31-37
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• 2022

본 논문은 일상생활에서 흔히 들을 수 있는 소리(비명소리, 박수 소리, 여러 명의 박수 소리, 자동차 지나가는 소리, 배경음 등)를 감지하는 음향 인식을 위하여, 신호처리 및 딥러닝을 적용하는 연구에 관한 것이다. 제안된 음향 인식에서는, 인식 정확도의 향상을 위해서 음향 파형의 스펙트럼, 음향 데이터의 증강, 2차원(2-D) 이미지 변환에 관한 기술들이 사용되었고, 예측의 정확도를 향상을 위한 앙상블 학습, Convolution Neural Network(CNN) 딥러닝 기술들이 적용된다. 제안된 음향 인식 기술은 실험을 통해 다양한 음향을 정확하게 인식할 수 있음을 보여준다.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.7-11
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• 2021

본 연구는 초등학교에서 쉽게 적용할 수 있는 소리 데이터를 활용한 블록 기반의 머신러닝 교육 프로그램을 설계하였다. 교육 프로그램은 ADDIE 모형에 따라 사전에 초등학교 교사 70명을 대상으로 실시한 요구 분석을 결과를 바탕으로 그 목표와 방향을 설계하였다. 머신러닝 포 키즈 중 블록 기반의 프로그래밍을 위해 스크래치를 사용하였고 소리 데이터를 활용하여 데이터값의 규칙성을 발견하고 인공지능의 원리를 학습하고 직접 문제를 해결하는 프로그래밍 과정에서 컴퓨팅 사고력을 향상할 수 있도록 교육 프로그램을 설계하였다. 추후의 연구에서 본 교육 프로그램을 적용하고 인공지능에 대한 태도와 컴퓨팅 사고력에 어떤 변화가 있는지 검증이 필요하다.

• 수산해양교육연구
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• 제16권2호
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• pp.163-170
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• 2004

The rapid exchanges of mobile computing environment and development of wireless communication are providing many effects for learning activity of students. Recently, PDA system developers which are studying memory capacity, communication speed and size of screen support techniques to be capable of learning from students in the wireless or moving environment. In this viewpoints, this paper has a purpose to design multimedia learning system to be able to do with sound lecture contents. The implemented system largely consists of two parts which have the teacher module and students module. The one manages learning progress of students, class management, bulletin board and etc. The other is capable of using studying and bulletin functions. The main idea of this research is focus to upgrade the effect of learning without almost treating the existing studies, which can be listening sound lecture and also seeing text and image at the same time.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• 한국해양공학회지
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• 제34권3호
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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.

• 한국음향학회지
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• 제35권2호
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• pp.143-148
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• 2016

깊은 신경망은 데이터의 특성을 효과적으로 나타낼 수 있는 방법으로 최근 많은 응용 분야에서 활용되고 있다. 하지만, 제한적인 양의 데이터베이스는 깊은 신경망을 훈련하는 과정에서 과적합 문제를 야기할 수 있다. 본 논문에서는 풍부한 양의 음성 혹은 음악 데이터를 이용한 전이학습을 통해 제한적인 양의 사운드 이벤트에 대한 깊은 신경망을 효과적으로 훈련하는 방법을 제안한다. 일련의 실험을 통해 제안하는 방법이 적은 양의 사운드 이벤트 데이터만으로 훈련된 깊은 신경망에 비해 현저한 성능 향상이 있음을 확인하였다.

• 로봇학회논문지
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• 제17권3호
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• pp.273-281
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• 2022

In this paper, we propose a robot sound recognition system that detects various sound events. The proposed system is designed to detect various sound events in real-time by using a microphone on a robot. To get real-time performance, we use a VGG11 model which includes several convolutional neural networks with real-time normalization scheme. The VGG11 model is trained on augmented DB through 24 kinds of various environments (12 reverberation times and 2 signal to noise ratios). Additionally, based on random forest algorithm, a decision logic is also designed to generate event signals for robot applications. This logic can be used for specific classes of acoustic events with better performance than just using outputs of network model. With some experimental results, the performance of proposed sound recognition system is shown on real-time device for robots.

• Smart Structures and Systems
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• 제29권3호
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.