Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique


  •   Chowdhury Shahriar Muzammel

  •   Mahmudul Hasan

  •   Khalil Ahammad

  •   Mousumi Hasan Mukti


Varieties of environmental sources of noise and distortion can degrade the quality of the speech signal in a communication system. This research work explores the effects of these interfering sounds on speech applications and introduces a technique for reducing their influence and enhancing the acceptability and intelligibility of the speech signal. In this work, a noise reduction system using single microphone method in time domain to improve SNR of noise contaminated speech is proposed. Traditional Spectral Subtraction method has been reviewed very well and the relationship with wiener filter is also illustrated. The Spectral Subtraction method has been generalized and the focus is put on reducing noise from speech in single channel signals. Voice Activity Detector (VAD) is ignored in this proposed system, because a-priori information about the noise is assumed. The research has been conducted using Gaussian White Noise and Color Noise. The experimental result shows a remarkable improvement in SNR for the generalized version and it is noticed that the result is very much satisfactory when white noises are added but the addition of color noise produces a comparatively poor improvement report. The system has been tested with eight different datasets and on an average, 65.27% improvement in SNR (Signal to Noise Ratio) for White Noise using Generalized Spectral Subtraction Method is achieved comparing with Traditional Spectral Subtraction Method. The average improvement in SNR for Color Noise recorded is 53.31%. The Generalized Spectral Subtraction method is shown to improve the speech quality and to improve SNR as well.

Keywords: Speech Enhancement, Spectral Subtraction, SNR, TIMIT-Database, Wiener Method


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How to Cite
Muzammel, C.S., Hasan, M., Ahammad, K. and Mukti, M.H. 2018. Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique. European Journal of Engineering and Technology Research. 3, 7 (Jul. 2018), 78–80. DOI: