• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (1)
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• pp.4-6
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• 2003

A holistic representation, such as sparse ceding or independent component analysis (ICA), was successfully applied to explain early auditory processing and sound classification. In contrast, Part-based representation is an alternative way of understanding object recognition in brain. In this paper. we employ the non-negative matrix factorization (NMF)［1］which learns parts-based representation for sound classification. Feature extraction methods from spectrogram using NMF are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

• The Journal of the Acoustical Society of Korea
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• 제27권2E호
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• pp.57-63
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• 2008

Stethoscopic auscultation is still one of the primary tools for the diagnosis of heart diseases due to its easy accessibility and relatively low cost. It is, however, a difficult skill to acquire. Many research efforts have been done on the automatic classification of heart sound signals to support clinicians in heart sound diagnosis. Recently, hidden Markov models (HMMs) have been used quite successfully in the automatic classification of the heart sound signal. However, in the classification using HMMs, there are so many heart sound signal types that it is not reasonable to assign a new class to each of them. In this paper, rather than constructing an HMM for each signal type, we propose to build an HMM for a set of acoustically-similar signal types. To define the classes, we use the KL (Kullback-Leibler) distance between different signal types to determine if they should belong to the same class. From the classification experiments on the heart sound data consisting of 25 different types of signals, the proposed method proved to be quite efficient in determining the optimal set of classes. Also we found that the class determination approach produced better results than the heuristic class assignment method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3384-3398
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• 2020

In recent years, as the environment has become an important issue in dealing with food, energy, and urban development, diverse environment-related applications such as environmental monitoring and ecosystem management have emerged. In such applications, automatic classification of animals using video or sound is very useful in terms of cost and convenience. So far, many works have been done for animal sounds classification using artificial intelligence techniques such as a convolutional neural network. However, most of them have dealt only with the sound of a specific class of animals such as bird sounds or insect sounds. Due to this, they are not suitable for classifying various types of animal sounds. In this paper, we propose a sound classification scheme based on a multi-feature network for classifying sounds of multiple species of animals. To do that, we first collected multiple animal sound datasets and grouped them into classes. Then, we extracted their audio features by generating mixed records and used those features for training. To evaluate the effectiveness of our scheme, we constructed an animal sound classification model and performed various experiments. We report some of the results.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2004년도 제5회 정기학술발표대회 논문집
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• pp.170-173
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• 2004

2004년 4월 23일부터 바닥충격음에 대한 법이 시행되어 신규설계에 반영되고 있다. 새롭게 제시된 법에 따르면 표준 바닥구조를 통해 최소 슬래브 두께가 180 mm로 제시되었으며 중량충격음에 대한 최소 기준도 함께 제시되었다. 또한 최소기준과는 별도로 경량 충격음에 대한 등급화가 제시되어 소비자의 요구를 수용할 수 있도록 하였다. 그러나 현장 시험을 통한 조사 결과 경량 충격음의 등급화 시공을 위한 방안이 제시되지 않음으로서 설계목표를 세우고도 이루지 못하는 문제가 예상되고 있다. 본 내용은 등급화 시공을 위해서 검토해야 할 내용을 정리하여 설계자와 건설사에 도움을 주고자 한 내용이다.

• 한국멀티미디어학회논문지
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• 제23권11호
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• pp.1406-1417
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• 2020

Sounds that have been evaluated to be effective in inducing sleep are helpful to reduce sleep disorders. Generally, several sounds have been verified the effects by brainwave experiments, but it cannot be considered on all users because of individual variation for effects. Moreover, the effectiveness for inducing sleep is not known for all new sounds made by creative activities. Therefore, new classification system is required to collect new effective sounds with considering personal brainwave characteristics. In this paper, we propose a new sound classification method by applying improved MinHash cluster to brain waves. The proposed method will classify them through whether it is effective for sleep care by evaluation his brainwave during listening for each sound. In order to prove effectiveness of the proposed classification method, we conducted accuracy experiment for sleep sound classification using verified sleep induction sound. In addition, we have compared time for existing method and proposed method. The former is scored 85% accuracy in the experiment. We confirmed the latter one that the average processing time was reduced to 70%. It is expected to be one of method for pre-screening whether it is effective when a new sound is introduced as a sound for sleep induction.

• Asian-Australasian Journal of Animal Sciences
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• 제28권4호
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• pp.592-598
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• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• 한국음향학회지
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• 제26권4호
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• pp.144-152
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• 2007

기존의 심음분류를 위한 연구들은 인공신경망을 이용하여 주로 이루어졌다. 그러나 심음신호의 통계적 특성을 분석 한 결과 HMM의 의한 신호모델링이 적합한 것으로 나타났다. 본 연구에서는 다양한 질병을 나타내는 심음신호를 HMM을 이용하여 모델링 하고 인식성능이 심음신호의 클러스터링에 따라서 많이 좌우되는 것을 알 수 있었다. 또한 실제 환경에서의 심음신호는 그 시작과 끝나는 시점이 정해지지 않은 연속신호이다. 따라서 HMM을 이용한 심음분류를 위해서는 연속적인 심음신호로부터 한 사이클의 분할된 심음을 추출할 필요성이 있다. 일반적으로 수동분할은 분할오류를 발생시키며 실시간 심음인식에 적합하지 않으므로 분할과정이 필요치 않는 ergodic형 HMM을 변형하여 사용할 것을 제안하였다. 그리고 제안된 HMM은 연속심음을 이용한 분류실험에서 매우 높은 성능을 보임을 알 수 있었다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권10호
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• pp.1412-1417
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• 2004

전체관적인 표현방법인 희소 코딩 또는 독릴 성분 분해(ICA)는 이전의 청각의 처리와 소리 분류의 작업을 해명하는데 성공적으로 적용되었다. 반대로 부분 기반 표현법은 뇌에서 물체를 인식하는 방법을 이해하는 또 다른 방법이다. 이 논문에서, 우리는 소리 분류의 작업에 부분기반 표현법을 학습시키는 비음수화 행렬 분해(NMF)(1) 방법을 적용하였다. 잡음이 존재할 때와 존재하지 않을 때 두 가지 상황에서, NMF를 이용하여 주파수-시간영역의 소리로부터 특징을 추출하는 방법을 설명한다. 실험결과에서는 NMF에 기반을 둔 특징이 ICA에 기반을 두어 추출한 특징보다 소리 분류의 성능을 향상시킴을 보여준다.

• 대한한의학회지
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• 제40권2호
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• pp.89-93
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• 2019

Objectives: There are substantial variations on the methods of identifying Sa-sang typology in clinical practice. This review aimed to describe the clinical experiences on the classification of Sa-sang typology based on index signs for practice of Tae-Geuk acupuncture. Methods: Core physical signs and interpretation of treatment response for the classification of Sa-sang typology are suggested based on 42-year clinical experiences of the expert (the first author). Results: Epigastric tenderness and hepatic dullness sound are the most important physical sign in the classification of Sa-sang typology. Clinical experiences indicate that there may be a positive association between the presence of epigastric tenderness and hepatic dullness sound. Four sets of acupuncture points are matched for four types of Sa-sang institution, respectively. Appropriate match will resolve epigastric tenderness and hepatic dullness sound, while this will not happen if inappropriate match is employed. Conclusion: I suggest that two physical signs (i.e., epigastric tenderness and hepatic dullness sound) are essential for the classification of Sa-sang typology in Tae-Geuk acupuncture.

• 한국음향학회지
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• 제29권2호
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• pp.133-140
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• 2010

연속 심음신호로부터 추출한 새로운 시간영역에서의 특징들을 추가하여 심장질환 분류의 성능을 개선한다. 기존에 사용되고 있는 켑스트럼 영역 특징인 멜주파수 켑스트럼 계수 (MFCC)에 심음 포락선, 심잡음 확률벡터, 심잡음 진폭값 변동으로 구성된 새로운 3종류의 시간영역 특징을 추가한다. 심장 질환 분류 및 검출 실험에서, 시간영역 특징의 분류 정확도에 대한 기여도를 평가하고 순차적 특징선택 방식을 이용하여 시간영역 특징을 선택한다. 선택된 특징들은 다층 퍼셉트론(MLP), support rector machine (SVM), extreme learning machine (ELM)와 같은 신경회로망 패턴 분류기에 대하여 의미있고 일관되게 분류 정확도를 개선함을 보여준다.