Adaptive Speech Spectrogram Approximation for Enhancement of Speech Signal
Manju Ramrao Bhosle1, Nagesh K N2, Ravi Chaurasia3
1Manju Ramrao Bhosle, Ph.D Scholar, Assistant Professor, Government Engineering College, Raichur VTU Belgavi (Karnataka), India.
2Dr. Nagesh K. N, Ph.D, Master Degree, Wireless Communication JNTUCE, Digital Electronics and Communication, Visvesvaraya Technological University, Belgaum (Karnataka), India.
3Ravi Chaurasia, Bachelor Degree, Department of Electronics & Communication, Visvesvaraya Technological University, Belgaum (Karnataka), India.
Manuscript received on 22 May 2019 | Revised Manuscript received on 12 June 2019 | Manuscript Published on 27 June 2019 | PP: 163-168 | Volume-8 Issue-1C May 2019 | Retrieval Number: A10280581C19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The process of speech enhancement tends to decrease the noise with keeping undistorted speech signal amplitude. There are several benefits of speech processing systems which comes with the some challenges. In this paper, we proposed ASSA technique that used to tackle the de-noising and dereverberation in a single channel speech signal. The model is processed using sparse representation prototype in order to perform the de-noising process, where it remove the noise that present in speech signal more thoroughly. Where matrix factorization and SIFT is used to model the speech signal spectrogram, a time-varying filter is used to minimalize the noise more effectively. The noise adaptive model is implemented via iterative updating parameters in order to approximate the lower reverberant speech signal in a SIFT domain. Afterwards, the proposed ASSA technique compute the variation in estimated speech signal in order to decrease the noise components and to predict the final speech magnitude. In order to evaluate the performance of proposed system it is compared with state-of-art techniques using some performance metrics.
Keywords: Adaptive Speech Spectrogram Approximation (ASSA), Short-Interval Fourier Transform (SIFT), Matrix Factorization, Noise, Dereverberation.
Scope of the Article: Soft computing Signal and Speech Processing