Combined image compression and denoising using wavelets
Image Communication
Formant tracking linear prediction model using HMMs and Kalman filters for noisy speech processing
Computer Speech and Language
On the optimal linear filtering techniques for noise reduction
Speech Communication
Subband Kalman filtering incorporating masking properties for noisy speech signal
Speech Communication
Temporally correlated source separation using variational Bayesian learning approach
Digital Signal Processing
Modulation-domain Kalman filtering for single-channel speech enhancement
Speech Communication
Object tracking from image sequences using adaptive models in fuzzy particle filter
Information Sciences: an International Journal
| Hi-index | 35.68 |
Scalar and vector Kalman filters are implemented for filtering speech contaminated by additive white noise or colored noise, and an iterative signal and parameter estimator which can be used for both noise types is presented. Particular emphasis is placed on the removal of colored noise, such as helicopter noise, by using state-of-the-art colored-noise-assumption Kalman filters. The results indicate that the colored noise Kalman filters provide a significant gain in signal-to-noise ratio (SNR), a visible improvement in the sound spectrogram, and an audible improvement in output speech quality, none of which are available with white-noise-assumption Kalman and Wiener filters. When the filter is used as a prefilter for linear predictive coding, the coded output speech quality and intelligibility are enhanced in comparison to direct coding of the noisy speech