Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Determinant-based two-channel noise reduction method using speech presence probability

음성존재확률을 이용한 행렬식 기반 2채널 잡음제거기법

  • Park, Jinuk (School of the Electrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Hong, Jungpyo (Department of Information and Communication Engineering, Changwon National University)
  • Received : 2022.02.22
  • Accepted : 2022.03.12
  • Published : 2022.05.31
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Abstract

In this paper, a determinant-based two-channel noise reduction method which utilizes speech presence probability (SPP) is proposed. The proposed method improves noise reduction performance from the conventional determinant-based two-channel noise reduction method in [7] by applying SPP to the Wiener filter gain. Consequently, the proposed method adaptively controls the amount of noise reduction depending on the SPP. For performance evaluation, the segmental signal-to-noise ratio (SNR), the perceptual evaluation of speech quality, the short time objective intelligibility, and the log spectral distance were measured in the simulated noisy environments considered various types of noise, reverberation, SNR, and the direction and number of noise sources. The experimental results presented that determinant-based methods outperform phase difference-based methods in most cases. In particular, the proposed method achieved the best noise reduction performance maintaining minimum speech distortion.

본 논문에서는 음성존재확률을 활용한 2채널 입력신호 상관행렬의 행렬식 기반 잡음제거 기법을 제안하였다. 제안한 기법은 음성존재확률을 이용해 기존의 행렬식 기반 2채널 잡음제거 기법의 위너 필터 이득을 음성과 잡음구간에 따라 적응적으로 조절함으로써 잡음제거 성능을 더욱 향상시키고자 하였다. 제안한 기법은 잡음 종류, 반향 조건, 신호대잡음비, 잡음원의 개수와 방향이 다양한 모의 환경에서 객관적 평가 방법을 통해 평가되었다. 실험 결과는 대부분의 실험환경에서 행렬식 기반의 기법들이 위상차를 기반으로 한 기법들보다 성능이 우수했고 특히 제안한 음성존재확률을 이용한 행렬식 기반 잡음제거기법이 음성 신호 왜곡을 최소화하면서 가장 우수한 잡음 제거 성능을 보였다.

Keywords

Acknowledgement

This research was supported by Changwon National University in 2021~2022.

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