Name
Abstract | ||
|---|---|---|
Periodogram smoothing of the received noisy signal is a chal- lenging problem in speech enhancement. We present a Bayesian approach, where the instantaneous periodogram is smoothed through an adaptive smoothing parameter. By updating sucient statistics using new sam- ples of the noisy signal, the smoothing parameter is adjusted on-line. The performance of the novel smoothing algorithm is studied in a speech en- hancement context. It is demonstrated that with respect to Mean Square Error, the proposed Bayesian smoothing algorithm performs better than the other non-Bayesian smoothing algorithms in higher signal-to-noise ra- tio environments. |
Year | Venue | Keywords |
|---|---|---|
2009 | ESANN | mean square error,bayesian approach |
Field | DocType | Citations |
Speech enhancement,Pattern recognition,Computer science,Mean squared error,Smoothing,Periodogram,Artificial intelligence,Additive smoothing,Bayesian probability | Conference | 1 |
PageRank | References | Authors |
0.47 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xueru Zhang | 1 | 22 | 3.61 |
| Alexander Ypma | 2 | 86 | 14.99 |
| Bert de Vries | 3 | 183 | 57.47 |