SPEECH QUALITY ENHANCEMENT USING FAST ADAPTIVE KALMAN FILTER
Author(s):
Gugulothu vidhyavathi, M A Himayath shamshi
Keywords:
Fast adaptive kalman Filter, speech enhancement ,Linear Prediction coding
Abstract
Due to the existence of the background noise speech quality and intelligibility might significantly decline, mainly when the speech signal is subjected to subsequent processing. Especially, automatic speech recognition (ASR) systems and voice coders were designed to perform on pure speech signals might be in effective due to the existence of background noise. The traditional Kalman filter algorithm executes a plenty of matrix operations and it needs to compute the AR (auto-regressive) model parameters, for speech enhancement. Usually, the standard Kalman filter algorithm is non-adaptive. The improved Kalman filter algorithm proposed in this paper removes the matrix calculations and decreases the computation time by constantly upgrading the first value of the state vector X (n). A coefficient factor is designed for adaptive filtering in order to rectify the evaluation of background noise. Speech enhancement techniques and its algorithms are attracted many researchers. The speech extension technique is one of the effective techniques to resolve the speech degenerated by noise. We presented Kalman filter-based algorithms with some enlargements, adjustments, and developments of foregoing work. In this paper a fast speech enhancement method for noisy speech signals is presented, which is based on improved Kalman filtering. Fast adaptive Kalman filter is designed for the removal of the noises in the signals. The process of denoising input speech signals is more helpful in the process of providing efficient sound system. Fast adaptive Kalman filtering is employed for the removal of the noises from the signal which is based on the prediction and estimation of the noise level in the signal. The input speech signal is denoised with the help of the fast adaptive Kalman filter. Simulation results show that the fast adaptive Kalman filtering algorithm is quite effective for speech enhancement.
Article Details
Unique Paper ID: 144870

Publication Volume & Issue: Volume 4, Issue 5

Page(s): 210 - 214
Article Preview & Download


Share This Article

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Go To Issue



Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies