The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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A Study on the Reconstruction of a Frame Based Speech Signal through Dictionary Learning and Adaptive Compressed Sensing

Adaptive Compressed Sensing과 Dictionary Learning을 이용한 프레임 기반 음성신호의 복원에 대한 연구

  • Received : 2012.08.17
  • Accepted : 2012.11.17
  • Published : 2012.12.28
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Abstract

Compressed sensing has been applied to many fields such as images, speech signals, radars, etc. It has been mainly applied to stationary signals, and reconstruction error could grow as compression ratios are increased by decreasing measurements. To resolve the problem, speech signals are divided into frames and processed in parallel. The frames are made sparse by dictionary learning, and adaptive compressed sensing is applied which designs the compressed sensing reconstruction matrix adaptively by using the difference between the sparse coefficient vector and its reconstruction. Through the proposed method, we could see that fast and accurate reconstruction of non-stationary signals is possible with compressed sensing.

압축센싱은 이미지, 음성신호, 레이더 등 많은 분야에 적용되고 있다. 압축센싱은 주로 통계적 특성이 시불변인 신호에 적용되고 있으며, 측정 데이터를 줄여 압축률을 높일수록 복원에러가 증가한다. 이와 같은 문제점들을 해결하기 위해 음성신호를 프레임 단위로 나누어 병렬로 처리하였으며, dictionary learning을 이용하여 프레임들을 sparse하게 만들고, sparse 계수 벡터와 그 복원값의 차를 이용하여 압축센싱 복원행렬을 적응적으로 만든 적응압축센싱을 적용하였다. 이를 통해 통계적 특성이 시변인 신호도 압축센싱을 이용하여 빠르고 정확한 복원이 가능함을 확인할 수 있었다.

Keywords

References

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