Upgraded NLMS algorithm for speech enhancement with sparse and dispersive impulse responses

To have the effective speech communication, the information should be clearly passed in a noise-free environment. However, in real-world environment, the existence of background noise degrades the performance of the system. Based on the blind source separation strategy, various adaptive algorithms a...

Full description

Saved in:
Bibliographic Details
Published in:Indian journal of physics 2021, Vol.95 (1), p.21-32
Main Authors: Sundaradhas, Selva Nidhyananthan, Panchama moorthy, Shanmuga Priya, Ramapackiyam, Shantha Selva Kumari
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive filters
Adaptive systems
Algorithms
Astrophysics and Astroparticles
Background noise
Dispersion
Impulse response
Mean square errors
Mean square values
Original Paper
Performance degradation
Performance enhancement
Physics
Physics and Astronomy
Signal processing
Signal to noise ratio
Speech processing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To have the effective speech communication, the information should be clearly passed in a noise-free environment. However, in real-world environment, the existence of background noise degrades the performance of the system. Based on the blind source separation strategy, various adaptive algorithms are designed and implemented using both dispersive impulse response and sparse impulse response. Even though the existing dual fast normalized least mean square algorithm works well under different noisy situations and gives a good performance, the problem is that it involves large number of processing steps. To overcome the complexity in finding the signal prediction parameter and to improve the performance of speech enhancement, we propose three adaptive filtering algorithms namely revised twofold rapid normalized least mean square algorithm, diminished twofold normalized least mean square algorithm and upgraded balanced twofold normalized least mean square algorithm (UBTNLMS). Taking the performance objective criteria into account, these algorithms have been tested for segmental signal-to-noise ratio, segmental mean square error, signal-to-noise ratio, mean square error and cepstral distance. On comparing the performance of the existing and proposed algorithms, UBTNLMS performs better than the other algorithms.
ISSN:0973-1458
0974-9845
DOI:10.1007/s12648-020-01688-5