Year: 2013
Type: article
Abstract: The goal of this paper is to investigate the speech signal enhancement using Kernel Affine Projection Algorithm (KAPA ) and Normalized KAPA.The removal of background noise is very important in many applications like speech recognition, telephone conversations, hearing aids, forensic, etc. Kernel adaptive filters shown good performance for removal of noise.If the evaluation of background noise is more slowly than the speech, i.e., noise signal is more stationary than the speech, we can easily estimate the noise during the pauses in speech.Otherwise it is more difficult to estimate the noise which results in degradation of speech.In order to improve the quality and intelligibility of speech, unlike time and frequency domains, we can process the signal in new domain like Reproducing Kernel Hilbert Space (RKHS) for high dimensional to yield more powerful nonlinear extensions.For experiments, we have used the database of noisy speech corpus (NOIZEUS).From the results, we observed the removal noise in RKHS has great performance in signal to noise ratio values in comparison with conventional adaptive filters.
Authors: Bolimera Ravi, Kishore Kumar T
Institution: National Institute of Technology Warangal
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2012600288