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산림 모니터링 및 재해관리를 위한 4IR 디지털 기술의 적용
이성희(Seonghee Lee),이후동(Hoodong Lee),김성원(Sungwon Kim),이영진(Youngjin Lee) 대한공간정보학회 2022 한국공간정보학회 학술대회 Vol.2022 No.5
우리나라 산림의 특징은 지형이 복잡하고 인력의 접근이 어려워 산림 모니터링 및 재해관리를 위한 4차 산업혁명 디지털 기술인 드론, 빅데이터, 사물인터넷, 인공지능과 딥러닝(deep learning) 등을 활용한 기술을 시스템에 적용하여 산림 모니터링 및 재해관리 업무에 일부 활용되고 있다. 본 연구에서는 산림 모니터링 및 재해관리를 위하여 4차 산업혁명 기술의 효율적인 적용방안을 검토하고자 한다.
숫자음의 스펙트럼 차이값과 상관계수를 이용한 화자인증 파라미터 연구
이후동,강선미,장문수,양병곤 한국음성과학회 2004 음성과학 Vol.11 No.3
Speaker identification system basically functions by comparing spectral energy of an individual production model with that of an input signal. This study aimed to develop a new speaker identification system from two parameters from the spectral energy of numeric sounds: difference sum and correlation coefficient. A narrow-band spectrogram yielded more stable spectral energy across time than a wide-band one. In this paper, we collected empirical data from four male speakers and tested the speaker identification system. The subjects produced 18 combinations of three-digit numeric sounds ten times each. Five productions of each three-digit number were statistically averaged to make a model for each speaker. Then, the remaining five productions were tested on the system. Results showed that when the threshold for the absolute difference sum was set to 1200, all the speakers could not pass the system while everybody could pass if set to 2800. The minimum correlation coefficient to allow all to pass was 0.82 while the coefficient of 0.95 rejected all. Thus, both threshold levels can be adjusted to the need of speaker identification system, which is desirable for further study.
녹음 환경의 차이에 따른 화자의 음원 특성 비교 : 발성유형지수 k를 중심으로
이후동,강선미,박한상,장문수 한국음성과학회 2003 음성과학 Vol.10 No.3
Spoken sound includes not only speaker's source but the characteristics of vocal tract and speech radiation. This paper is based on the theory of Park[1], who proposes the Phonation Type Index k; a variable that shows the characterisic of speaker's source excluding those of speaker's vocal tract and speech radiation. With Park's theory, we collect data by changing recording environments and expanding experimental data, and analyze the data collected to see whether or not the PTI k shows good discriminating power as a variable for speaker recognition. In the experiment, we repeatedly record 8 sentences ten times for each of 5 males in the environment of a recording room and an office, extract PTI k for each speaker, and measure the discriminating power for each speaker by using the value of PTI k. The result shows that PTI k has the excellent discriminating power of speakers. We also confirm that, even if the recording environment is changed, PTI k shows similar results.