• Title/Summary/Keyword: sound classification

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NMF-Feature Extraction for Sound Classification (소리 분류를 위한 NMF특징 추출)

  • Yong-Choon Cho;Seungin Choi;Sung-Yang Bang
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.10a
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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.

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Class Determination Based on Kullback-Leibler Distance in Heart Sound Classification

  • Chung, Yong-Joo;Kwak, Sung-Woo
    • The Journal of the Acoustical Society of Korea
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    • v.27 no.2E
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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.

Animal Sounds Classification Scheme Based on Multi-Feature Network with Mixed Datasets

  • Kim, Chung-Il;Cho, Yongjang;Jung, Seungwon;Rew, Jehyeok;Hwang, Eenjun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.8
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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.

A Working plan of classification against Floor Impact Sound (바닥충격음의 등급화 시공방안)

  • Jeong Gab-Cheol
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • 2004.11a
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    • pp.170-173
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    • 2004
  • The law about floor impact sound goes into effect from March 23th in 2004 and is applying to new designs. According to the new law, the minimal of slab thickness is 180mm by standard floor structure and the new law presents about the minimal standard about heavy-weight impact sound. Also, It presents about classification of light-weight sound separate the minimal standard, so demand of consumers can be accepted. But a working plan of classification about light-weight sound is not presented in accordance with field test, so the problem that design can't be achieved although the aim of design is formed. This study shows contents to investigate for working of classification and will be helpful to designers and construction corporations.

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Classification Method of Sleep Induction Sounds in Sleep Care Service based on Brain Wave (뇌파에 기반한 수면케어 서비스에서 수면유도음향의 분류기법)

  • Wi, Hyeon Seung;Lee, Byung Mun
    • Journal of Korea Multimedia Society
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    • v.23 no.11
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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.

Stress Detection and Classification of Laying Hens by Sound Analysis

  • Lee, Jonguk;Noh, Byeongjoon;Jang, Suin;Park, Daihee;Chung, Yongwha;Chang, Hong-Hee
    • Asian-Australasian Journal of Animal Sciences
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    • v.28 no.4
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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.

Automatic Classification of Continuous Heart Sound Signals Using the Statistical Modeling Approach (통계적 모델링 기법을 이용한 연속심음신호의 자동분류에 관한 연구)

  • Kim, Hee-Keun;Chung, Yong-Joo
    • The Journal of the Acoustical Society of Korea
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    • v.26 no.4
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    • pp.144-152
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    • 2007
  • Conventional research works on the classification of the heart sound signal have been done mainly with the artificial neural networks. But the analysis results on the statistical characteristic of the heart sound signal have shown that the HMM is suitable for modeling the heart sound signal. In this paper, we model the various heart sound signals representing different heart diseases with the HMM and find that the classification rate is much affected by the clustering of the heart sound signal. Also, the heart sound signal acquired in real environments is a continuous signal without any specified starting and ending points of time. Hence, for the classification based on the HMM, the continuous cyclic heart sound signal needs to be manually segmented to obtain isolated cycles of the signal. As the manual segmentation will incur the errors in the segmentation and will not be adequate for real time processing, we propose a variant of the ergodic HMM which does not need segmentation procedures. Simulation results show that the proposed method successfully classifies continuous heart sounds with high accuracy.

Classification of General Sound with Non-negativity Constraints (비음수 제약을 통한 일반 소리 분류)

  • 조용춘;최승진;방승양
    • Journal of KIISE:Software and Applications
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    • v.31 no.10
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    • pp.1412-1417
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    • 2004
  • Sparse coding or independent component analysis (ICA) which is a holistic representation, was successfully applied to elucidate early auditor${\gamma}$ processing and to the task of sound classification. In contrast, parts-based representation is an alternative way o) understanding object recognition in brain. In this thesis we employ the non-negative matrix factorization (NMF) which learns parts-based representation in the task of sound classification. Methods of feature extraction from the spectro-temporal sounds using the NMF in the absence or presence of noise, are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

Classification of Sa-sang typology based on index signs for Tae-Geuk acupuncture: a narrative review (태극침지표에 따른 사상체질감별에 대한 객관적 방법연구)

  • Kim, Jae-kyu
    • The Journal of Korean Medicine
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    • v.40 no.2
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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.

New Temporal Features for Cardiac Disorder Classification by Heart Sound (심음 기반의 심장질환 분류를 위한 새로운 시간영역 특징)

  • Kwak, Chul;Kwon, Oh-Wook
    • The Journal of the Acoustical Society of Korea
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    • v.29 no.2
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    • pp.133-140
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    • 2010
  • We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).