Filtering of Audio Signals Using Discrete Wavelet Transforms
| dc.contributor.author | Nigam, H.K. | |
| dc.contributor.author | Srivastava, Hari M. | |
| dc.date.accessioned | 2023-10-08T14:50:39Z | |
| dc.date.available | 2023-10-08T14:50:39Z | |
| dc.date.copyright | 2023 | en_US |
| dc.date.issued | 2023 | |
| dc.description.abstract | Nonlinear diffusion has been proved to be an indispensable approach for the removal of noise in image processing. In this paper, we employ nonlinear diffusion for the purpose of denoising audio signals in order to have this approach also recognized as a powerful tool for audio signal processing. We apply nonlinear diffusion to wavelet coefficients obtained from different filters associated with orthogonal and biorthogonal wavelets. We use wavelet decomposition to keep signal components well-localized in time. We compare denoising results using nonlinear diffusion with wavelet shrinkage for different wavelet filters. Our experiments and results show that the denoising is much improved by using the nonlinear diffusion process. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.identifier.citation | Nigam, H. K., & Srivastava, H. R. (2023). Filtering of Audio Signals Using Discrete Wavelet Transforms. Mathematics, 11(19), 4117. https://doi.org/10.3390/math11194117 | en_US |
| dc.identifier.uri | https://doi.org/10.3390/math11194117 | |
| dc.identifier.uri | http://hdl.handle.net/1828/15493 | |
| dc.language.iso | en | en_US |
| dc.publisher | Mathematics | en_US |
| dc.subject | wavelet decomposition | en_US |
| dc.subject | wavelet shrinkage | en_US |
| dc.subject | nonlinear diffusion | en_US |
| dc.subject | discrete wavelet transform | en_US |
| dc.subject | wavelet filters | en_US |
| dc.title | Filtering of Audio Signals Using Discrete Wavelet Transforms | en_US |
| dc.type | Article | en_US |