• 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)

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.193-198
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• 2019

This study analyzed the changes of vowel formant, voice intensity, and fundamental frequency of vowels for 11 months using acoustochemical spectrogram analysis of women diagnosed with amyotrophic lateral sclerosis (ALS). The test word was a vowel /a, i, u/ and a diphthong /h + ja + da/, /h + wi + da/, and /h +ɰi+ da/. Speech data were collected through the word reading task presented on the monitor using 'Alvin' program, and the recording environment was set to 5,500 Hz for the nyquist frequency and 11,000 Hz for the sampling rate. The records were analyzed by using spectrograms to vowel formants, voice intensity, and fundamental frequency. As a result of analysis, the fundamental frequency and intensity of the ALS process were decreased and the formant slope of the diphthong was decreased rather than the formant change in the vowel. This result suggests that the vowel distortion of ALS due to disease progression is due to the decrease of tongue and jaw co morbidity.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.375-378
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• 1998

본 연구의 목적은 한국에 존재하는 세 종류 파찰음/ㅈ,ㅊ, ㅉ/의 시간적.공간적 조음 특성을 전자구개도와 스펙트로그램 분석을 통하여 면밀히 검토해 보려는 것이다. 이를 위하여 두 실험이 행해 졌는데, 조음 음성학적인 실험(전자구개도를 이용한 실험)에는 한 명의 피험자가, 그리고 음향 음성학적인 실험(음향 자료의 스펙트로그램 분석)에는 세 명의 피험자가 발화한 자료가 이용되었다. 대상이 되는 세 자음들은 /ㅏ_ㅏ/, /ㅡ_ㅡ/, /ㅣ_ㅣ/ 등 세 모음 사이에서 발화되었으나, 모음이 자음의 조음에 미치는 영향, 즉 모음_자음 동시조음은 본 연구의 범위에서 제외되었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.53-54
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• 2010

본 논문에서는 단일 채널 디지털 오디오 신호에서 스펙트로그램과 영상 처리 기법을 이용하여 크래클 잡음을 검출하는 알고리즘을 제안한다. 오디오 신호의 주파수 특성을 효율적으로 분석하기 위해 스펙트로그램을 특정 컬러맵을 이용하여 컬러 영상으로 변환한 후 영상 처리 기법을 적용하여 크래클 잡음이 존재하는 구간을 검출하여 디지털 오디오 복원에 이용한다. 특히 고전영화에 나타나는 크래클 잡음은 에너지와 신호 길이가 음성이나 음악 신호와 유사하여 기존의 스펙트럴 음성 검출 기법으로는 검출에 어려움이 있다. 이에 비해 스펙트로그램 영상에서는 크래클 잡음이 다른 신호들과 구분되는 특성을 나타내므로 영상 처리 기법을 적용하여 경계 검출과 Hough 변환에 의한 선 검출을 이용하여 크래클 잡음을 검출한다. 제안된 알고리즘은 고전 영화 디지털 오디오 복원에 적용하였으며 크래클 잡음 검출에 우수한 성능을 보여준다.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3
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• pp.197-205
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• 2004

This paper represents the results of interpretation on the cause of sign changing of the striation slopes appearing in the range-frequency domain spectrogram of ship-radiated noise measured in a shallow sea. Striation patterns and dispersion characteristics simulated from a numerical model based on mode theory at various seabed conditions show that the sign changing of the striation slopes appearing in measured signal is caused by the shear property of seabed. more specifically by the shear property of the basement lying below the sediment which is estimated about 3±1m thick.

• 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.

• Journal of IKEEE
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• v.22 no.4
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• pp.1044-1049
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• 2018

CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And it shows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzing the spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing the targets and used for learning CNN. The spectrogram of the signal is divided into frames and used as inputs. As a result, it was possible to classify the target and non-target using the characteristic that the classification results of the seven classes corresponding to the target signal sequentially appear only at the position of the target signal.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.939-941
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• 2021

사람은 표정, 음성, 말 등을 통해 감정을 표출한다. 본 논문에서는 화자의 음성데이터만을 사용하여 감정을 분류하는 방법을 제안한다. 멜 스펙트로그램(Mel-Spectrogram)을 이용하여 음성데이터를 시간에 따른 주파수 영역으로 변화한다. 멜 스펙트로그램으로 변환된 데이터를 CNN을 이용하여 특징 벡터화한 후 Bi-Directional LSTM을 이용하여 화자의 발화 시간 동안 변화되는 감정을 분석한다. 마지막으로 완전 연결 네트워크를 통해 전체 감정을 분류한다. 감정은 Anger, Excitement, Fear, Happiness, Sadness, Neutral로, 총 6가지로 분류하였으며 데이터베이스로는 상명대 연구팀에서 구축한 한국어 음성 감정 데이터베이스를 사용하였다. 실험 결과 논문에서 제안한 CNN-LSTM 모델의 정확도는 88.89%로 측정되었다.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.397-404
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• 2011

In this paper, we were carried out experiments to apply parameter of voice analysis to measure changing characteristic articulator according to inhale the helium gas. The helium gas was used to overcome air embolism nitrogen gas to deal a fatal blow in body nitrogen gas by diver. However, the helium gas has been much trouble interpretation about abnormal voice of diver to cause squeaky voice of low articulation. Therefor, we was carried out experiments about pitch and spectrogram measurement, analysis based on to influence in acoustic organs before and after of inhaled helium gas.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.977-985
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• 2016

In this paper, we propose a new method for on-line genre classification using spectrogram and deep neural network. For on-line processing, the proposed method inputs an audio signal for a time period of 1sec and classifies its genre among 3 genres of speech, music, and effect. In order to provide the generality of processing, it uses the spectrogram as a feature vector, instead of MFCC which has been widely used for audio analysis. We measure the performance of genre classification using real TV audio signals, and confirm that the proposed method has better performance than the conventional method for all genres. In particular, it decreases the rate of classification error between music and effect, which often occurs in the conventional method.