Name
Abstract | ||
|---|---|---|
Compared with high sample-rate speeches, low sample-rate speeches lose all high frequency components that outrange the Nyquist frequency, which might severely impair the speeches' sound effects. To address this problem, this paper proposes a novel High-frequency (HF) restoration method of low sample-rate speech based on Bayesian inference, which turns the restoration problem into a maximizing a posteriori estimation. With this method, the relation between high frequency components and low frequency components is first extracted from the training set. The compatibility between neighboring audio frames is also modelled by a one dimensional Markov Random Field. Then the extracted knowledge is adopted in reconstructing the original high sample-rate signal for the testing low sample-rate audio. Experiments prove the applicability and effectiveness of this method. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11551188_60 | ICAPR (1) |
Keywords | Field | DocType |
nyquist frequency,high sample-rate speech,audio frame,bayesian method,testing low sample-rate audio,low sample-rate speech,high frequency component,high-frequency restoration,low frequency component,restoration problem,original high sample-rate signal,restoration method,low frequency,high frequency,bayesian inference | Speech enhancement,Markov process,Bayesian inference,Computer science,Markov random field,Nyquist frequency,Sampling (signal processing),A priori and a posteriori,Speech recognition,Bayesian probability | Conference |
Volume | ISSN | ISBN |
3686 | 0302-9743 | 3-540-28757-4 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunpeng Xu | 1 | 21 | 4.44 |
| Changshui Zhang | 2 | 5506 | 323.40 |
| Naijiang Lu | 3 | 24 | 3.74 |