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Bayesian Sparse Linear Prediction with Pearson Type VII Distribution
| Content Provider | Scilit |
|---|---|
| Author | Wan, Hongjie Zhang, Haiyun |
| Copyright Year | 2019 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering The speech signal can be modelled as AR models with an innovation noise model. The Pearson type VII distribution is used to model the real excitation. Variational Bayesian framework is used to estimate the posteriors of the AR coefficients and noise model parameters. The model is not conjugate, so MCMC is embed into VB framework to estimate the degree of freedom (DOF) parameter of Pearson type VII distribution. The model order selection is carried out by setting ARD priors on the coefficients. Simulation is carried out on synthetic and real data, the results show that the algorithm performs well for linear prediction both for synthetic data and speech signal, and the results are better than using least square method. |
| Related Links | https://iopscience.iop.org/article/10.1088/1757-899X/646/1/012065/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/646/1/012065 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 1 |
| Volume Number | 646 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-10-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Pearson Type Vii Type Vii Distribution Linear Prediction |
| Content Type | Text |
| Resource Type | Article |
