TITLE:
Noise Removal in Speech Processing Using Spectral Subtraction
AUTHORS:
Marc Karam, Hasan F. Khazaal, Heshmat Aglan, Cliston Cole
KEYWORDS:
Speech Processing, Spectral Subtraction, Noise Removal, Fast Fourier Transform, Inverse Fast Fourier Transform
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.5 No.2,
May
16,
2014
ABSTRACT: Spectral
subtraction is used in this research as a method to remove noise from noisy
speech signals in the frequency domain. This method consists of computing the
spectrum of the noisy speech using the Fast Fourier Transform (FFT) and
subtracting the average magnitude of the noise spectrum from the noisy speech
spectrum. We applied spectral subtraction to the speech signal “Real graph”. A
digital audio recorder system embedded in a personal computer was used to
sample the speech signal “Real graph” to which we digitally added vacuum
cleaner noise. The noise removal algorithm was implemented using Matlab
software by storing the noisy speech data into Hanning time-widowed
half-overlapped data buffers, computing the corresponding spectrums using the
FFT, removing the noise from the noisy speech, and reconstructing the speech
back into the time domain using the inverse Fast Fourier Transform (IFFT). The
performance of the algorithm was evaluated by calculating the Speech to Noise Ratio
(SNR). Frame averaging was introduced
as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the
Hanning time windows, various degrees of data buffers overlapping, and various
numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using
one-fourth overlapped data buffers with 128 points Hanning windows and no
frames averaging leads to the best performance in removing noise from the noisy
speech.