Name
Abstract | ||
|---|---|---|
When a single sequence of noisy observations is available, the autoregressive (AR)-model based methods using Kalman-filter make it possible to enhance speech. However, the estimation of the AR parameters is required, but is still a challenging problem as the signal is corrupted by an additive noise. In this paper, we propose to both estimate the signal and the AR parameters by developing a recursive instrumental variable-based approach. Avoiding a non linear approach such as the EKF, this method involves two conditionally linked Kalman filters running in parallel. Once a new observation is available, the first filter uses the latest estimated AR parameters to estimate the signal, while the second filter uses the estimated signal to update the AR parameters. A comparative study between existing speech enhancement methods is completed. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/MMSP.2004.1436571 | MMSP |
Keywords | Field | DocType |
Kalman filters,autoregressive processes,noise,parameter estimation,speech enhancement,Kalman filters based instrumental variable technique,additive noise,autoregressive-model based method,estimated autoregressive parameter,signal estimation,speech enhancement method | Speech enhancement,Autoregressive model,Extended Kalman filter,Nonlinear system,Pattern recognition,Computer science,Instrumental variable,Kalman filter,Artificial intelligence,Estimation theory,Recursion | Conference |
ISBN | Citations | PageRank |
0-7803-8578-0 | 2 | 0.39 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Labarre | 1 | 2 | 1.75 |
| E. Grivel | 2 | 74 | 8.43 |
| Mohamed Najim | 3 | 149 | 32.29 |
| Ezio Todini | 4 | 2 | 0.39 |