The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
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Improved time delay estimation by adaptive eigenvector decomposition for two noisy acoustic sensors

잡음이 있는 두 음향 센서를 이용한 시간 지연 추정을 위한 향상된 적응 고유벡터 추정 기반 알고리즘

  • Lim, Jun-Seok (Department of Electrical Engineering, Sejong University)
  • 임준석 (세종대학교 전자정보통신공학과)
  • Received : 2018.08.24
  • Accepted : 2018.11.21
  • Published : 2018.11.30
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Abstract

Time delay estimation between two acoustic sensors is widely used in room acoustics and sonar for target position estimation, tracking and synchronization. A cross-correlation based method is representative for the time delay estimation. However, this method does not have enough consideration for the noise added to the receiving acoustic sensors. This paper proposes a new time delay estimation method considering the added noise on the receiver acoustic sensors. From comparing with the existing GCC (Generalized Cross Correlation) method, and adaptive eigen decomposition method, we show that the proposed method outperforms other methods for a colored signal source in the white Gaussian noise condition.

서로 떨어져 설치된 두 개의 음향 센서에 도달하는 신호의 상호 지연 시간을 추정하는 것은 실내 음향과 소나 등에서 목표물 위치 추정 문제나 추적 및 동기화에 이르기까지 다방면에서 쓰이고 있다. 시간 지연을 구하는 방법에서는 두 수신 신호 사이의 상호 상관을 이용한 방법이 대표적이다. 그러나 이 방법은 수신 음향 센서에 잡음이 부과 되는 것에 충분한 고려가 없었다. 본 논문은 수신 음향 센서에 모두 잡음이 부과된 경우를 고려한 새로운 시간 지연 추정 방법을 제안한다. 기존의 일반 상호 상관법과 적응 고유치 분석법과 비교를 통해서 새로 제안한 알고리즘이 유색 신호에 부가된 가우시안 잡음환경에서 우수성이 있음을 확인한다.

Keywords

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Fig. 1. Time delay estimation modeling by system identification approach.

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Fig. 2. Time delay channel model between two received signals.

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Fig. 3. Corrected time delay estimation system diagram for added noise sensors.

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Fig. 4. Time delay estimation system diagram for added noise sensors.

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Fig. 5. Performance comparison in case of white gaussian signal source (-o-: proposed algorithm, -x-: GCC-PHAT, -□-: Adaptive eigenvector decomposition method).

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Fig. 6. Performance comparison in case of colored signal source (-o-: proposed algorithm, -x-: GCCPHAT, -□-: Adaptive eigenvector decomposition method).

GOHHBH_2018_v37n6_499_f0007.png 이미지

Fig. 7. Comparison of TDE in T60 = 250 ms and SNR = 10 dB. (a) proposed algorithm (b) GCC[9] (c) adaptive EVD[12].

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Fig. 8. Comparison of TDE in T60 = 610 ms and SNR = 10 dB. (a) proposed algorithm (b) GCC[9] (c) adaptive EVD[12].

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