• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.421-430
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• 2018

Many voice recognition systems use methods such as MFCC, HMM to acknowledge human voice. This recognition method is designed to analyze only a targeted sound which normally appears between a human and a device one. However, the recognition capability is limited when there is a group sound formed with diversity in wider frequency range such as dog barking and indoor sounds. The frequency of overlapped sound resides in a wide range, up to 20KHz, which is higher than a voice. This paper proposes the new recognition method which provides wider frequency range by conjugating the Wideband Sound Spectrogram and the Keras Sequential Model based on DNN. The wideband sound spectrogram is adopted to analyze and verify diverse sounds from wide frequency range as it is designed to extract features and also classify as explained. The KSM is employed for the pattern recognition using extracted features from the WSS to improve sound recognition quality. The experiment verified that the proposed WSS and KSM excellently classified the targeted sound among noisy environment; overlapped sounds such as dog barking and indoor sounds. Furthermore, the paper shows a stage by stage analyzation and comparison of the factors' influences on the recognition and its characteristics according to various levels of noise.

• Proceedings of the KSLP Conference
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• 1995.11a
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• pp.111-127
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• 1995

한국어 모음과 자음의 파형, 스펙트로그램을 통해 다음 사항을 중점적으로 다룬다. - 모음과 자음의 조음 및 음향적 특성, - 모음의 좁힙점과 음향적 특성, - /모음＋모음/과 /반모음＋모음/의 차이, - 자음의 조음 방법 및 조음장소에 따른 음향적 특성, - 음성환경에 따른 음향적 특성, - 유/무성에 따른 음향적 특성, - 연/경성에 따른 음향적 특성, - 동시조음에 따른 음향적 특성, - 소리의 길이 (중략)

• The Journal of the Acoustical Society of Korea
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• v.27 no.8
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• pp.418-426
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• 2008

In this paper, we propose an algorithm for the frequency channel segmentation using peaks and valleys in spectrogram. The frequency channel segments means that local groups of channels in frequency domain that could be arisen from the same sound source. The proposed algorithm is based on the smoothed spectrum of the input sound. Peaks and valleys in the smoothed spectrum are used to determine centers and boundaries of segments, respectively. To evaluate a suitableness of the proposed segmentation algorithm before that the grouping stage is applied, we compare the synthesized results using ideal mask with that of proposed algorithm. Simulations are performed with mixed speech signals with narrow band noises, wide band noises and other speech signals.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.343-345
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• 2020

본 논문에서는 선택적 노이즈 캔슬링을 위한 환경 인지 기술을 제안한다. 기존의 노이즈 캔슬링은 모든 소리를 구분 없이 차단하여 여러 가지 문제를 유발할 수 있으며 공통된 노이즈 캔슬링 동작으로 각 소음에 최적화된 성능을 보장할 수 없다. 이러한 문제를 해결하기 위해 제안하는 방법은 대표적 오디오 특성인 멜-스펙트로그램과 스펙트로그램 기반의 시간적 특성 벡터를 사용하여 환경 인지를 진행한다. 본 논문에서는 attack, rotation, sawing으로 구성된 3가지 소음과 speech, tonal로 구성된 2가지 비 소음으로 총 5가지 클래스를 분류한다. 제안하는 방법에서 특성 벡터로 멜-스펙트로그램만을 사용했을 때 87.5%의 분류 성능을 보였으며, 스펙트로그램 기반의 시간적 특성을 추가했을 때 분류 성능이 91.2%로 향상되었다.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.964-966
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• 2020

전염성이 매우 강한 돼지 호흡기 질병을 빠른 시간 내에 정확하게 탐지하지 못한다면 해당 돈사는 물론 타지역으로 전파되어 심각한 경제적 손실이 발생한다. 본 논문은 이와 같은 돼지 호흡기 질병을 저가격의 임베디드 보드에서도 탐지가 가능한 시스템을 제안한다. 해당 시스템은 돈사에 설치한 소리센서로부터 돼지의 이상 소리를 자동으로 탐지한 후, 탐지한 소리 시그널을 스펙트로그램으로 변환한다. 마지막으로, 스펙트로그램은 딥러닝 알고리즘에 적용되어 돼지 호흡기 질병을 탐지 및 식별한다. 이 때, 일반 컴퓨터 환경에 비해 비용 부담이 적은 임베디드 환경에서 실행되기 위하여 경량 딥러닝 모델인 MnasNet 을 사용하였으며, 임베디드 보드인 NVIDIA TX-2 에서 해당 시스템의 호흡기 질병 식별 성능을 확인한 결과 높은 탐지 성능과 실시간 탐지가 가능함을 확인하였다.

• Smart Media Journal
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• v.6 no.4
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• pp.9-16
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• 2017

The railway point machine is an especially important component that changes the traveling direction of a train. Failure of the point machine may cause a serious railway accident. Therefore, early detection of failures is important for the management of railway condition monitoring systems. In this paper, we propose a noise-robust anomaly detection method in railway condition monitoring systems using sound data. First, we extract feature vectors from the spectrogram image of sound signals and convert it into modulation feature to ensure robust performance, and lastly, use the support vector machine (SVM) as an early anomaly detector of railway point machines. By the experimental results, we confirmed that the proposed method could detect the anomaly conditions of railway point machines with acceptable accuracy even under noisy conditions.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.459-466
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• 2020

The sound collected through the multi-rotor unmanned aerial vehicle (UAV) includes the ego noise generated by the motor or propeller, or the wind noise generated during the flight, and thus the quality is greatly impaired. In a multi-rotor UAV environment, both the magnitude and phase of the target sound are greatly corrupted, so it is necessary to enhance the sound in consideration of both the magnitude and phase. However, it is difficult to improve the phase because it does not show the structural characteristics. in this study, we propose a CNN-based complex spectrogram enhancement method that removes noise based on complex spectrogram that can represent both magnitude and phase. Experimental results reveal that the proposed method improves enhancement performance by considering both the magnitude and phase of the complex spectrogram.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.666-668
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• 2018

최근 반려동물을 키우는 가구 수의 증가와 함께, 반려묘에 대한 관심도 상당히 증가하고 있다. 특히 반려인은 반려묘와의 원활한 의사소통과 교감을 바라지만 반려묘의 세세한 감정 상태를 24시간 내내 파악하는 것은 어려운 일이다. 본 논문에서는 반려묘의 울음소리에 많은 감정 및 상태 정보가 담겨있는 것에 착안하여, 반려묘의 울음소리를 기반으로 감정을 분류하는 시스템을 제안한다. 제안된 시스템은 먼저, 이미 수집된 소리 데이터를 데이터 증폭 방법론을 이용하여 데이터를 확장 한 후, 해당 소리들의 멜 스펙트로그램 정보를 추출한다. 이를 시계열 정보 처리에 효과적인 LSTM에 적용하여 반려묘의 감정 상황을 식별할 수 있도록 학습을 수행한다. 실험 결과, 반려묘의 감정 상태 분류의 가능성을 확인하였다.