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Noise Suppression Method Based on Modulation Spectrum Analysis

  • Takuto IsoyamaEmail author
  • Masashi UnokiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11096)

Abstract

Conventional methods for noise suppression can successfully reduce stationary noise. However, non-stationary noise such as intermittent and impulsive noise cannot be sufficiently suppressed since these methods do not focus on temporal features of noise. This paper proposes a method for suppressing both stationary and non-stationary noise based on modulation spectrum analysis. Modulation spectra (MS) of the stationary, intermittent, and impulsive noise were investigated by using the time/frequency/modulation analysis techniques to characterize the MS features. These features were then used to suppress the stationary and non-stationary noise components from the observed signals. Using the proposed method, the direct-current components of the MS in the stationary noise, harmonicity of the MS in the intermittent noise, and higher modulation-frequency components of the MS in the impulsive noise were removed. The following advantages of the proposed method were confirmed: (1) sound pressure level of the noise was dramatically reduced, (2) signal-to-noise ratio of the noisy speech was improved, and (3) loudness, sharpness, and roughness of the restored speech were enhanced. These results indicate that the stationary as well as non-stationary noise can be successfully suppressed using the proposed method.

Keywords

Noise suppression Modulation spectrum Non-stationary noise Gammatone filterbank Psychoacoustical sound-quality index 

Notes

Acknowledgments

This work was supported by the Secom Science and Technology Foundation by the Suzuki Foundation, and by a Grant in Aid for Innovative Areas (No. 16H01669, and 18H05004) from MEXT, Japan.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Japan Advanced Institute of Science and TechnologyNomiJapan

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