Name
Abstract | ||
|---|---|---|
In this paper, we propose a Bayesian approach for the estimation of the short-term predictor parameters of speech and noise, from the noisy observation. The resulting estimates of the speech and noise spectra can be used in a Wiener filter or any state-of-the-art speech enhancement system. We utilize a-priori information about both speech and noise in the form of trained codebooks of linear predictive coefficients. In contrast to current Bayesian estimation approaches that consider the excitation variances as part of the a-priori information, in the proposed method they are computed analytically based on the observation at hand. Consequently, the method performs well in nonstationary noise conditions. Experimental results confirm the superior performance of the proposed method compared to existing Bayesian approaches, such as those based on hidden Markov models. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1415304 | 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING |
Keywords | Field | DocType |
bayesian approach,background noise,linear predictive coding,acoustic noise,hidden markov models,parameter estimation,hidden markov model,maximum likelihood,spectrum,wiener filter,speech coding,learning artificial intelligence,random variable,performance,minimum mean square error,maximum likelihood estimation,computer and information science,bayesian methods | Wiener filter,Noise,Speech enhancement,Background noise,Speech coding,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Estimation theory,Hidden Markov model,Linear predictive coding | Conference |
ISSN | Citations | PageRank |
1520-6149 | 11 | 0.97 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sriram Srinivasan | 1 | 379 | 27.92 |
| Jonas Samuelsson | 2 | 165 | 11.19 |
| W. Bastiaan Kleijn | 3 | 1110 | 106.92 |