We study some speech enhancement algorithms based on the iterative Wiener filtering method due to LimOppenheim, where the AR spectral estimation of the speech is carried out using a second-order analysis. But in our algorithms we consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors, providing a behavior comparison between cumulant algorithms and classical autocorrelation one. Some results are presented considering A WGN that allows the best improvement and those noises (diesel engine and reactor noises) that leads to the worst one. An exhaustive empirical listening test shows that cumulant algorithms outperform the original autocorrelation algorithm, specially at low SNR.

Peer Reviewed