Name
Abstract | ||
|---|---|---|
A novel speech enhancement system is presented which exploits a codebook for noise estimation. In contrast to state-of-the-art noise estimators which usually rely on the assumption that the noise signal is only slightly timevarying, codebook approaches allow also non-stationary environments. The basic concept of the proposed codebook noise estimation is a superposition of a scaled speech and noise codebook entry. In order to be independent of a priori noise knowledge, the new estimator is able to learn new noise types online. Training vectors for codebook updates are identified using a speech activity detector (VAD) and a codebook mismatch measure. The VAD is realized as part of the codebook matching. A Wiener filter or any state-of-the-art weighting rule can be applied subsequently for speech enhancement. Experiments confirmed that the new system is able to learn new noise types and provides improved performance compared to state-of-the-art algorithms. |
Year | Venue | Field |
|---|---|---|
2014 | ITG Symposium on Speech Communication | Speech enhancement,Computer science,Speech recognition,Codebook |
DocType | Citations | PageRank |
Conference | 2 | 0.42 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florian Heese | 1 | 50 | 5.25 |
| Christoph Matthias Nelke | 2 | 64 | 6.21 |
| Niermann, Markus | 3 | 2 | 1.10 |
| Peter Vary | 4 | 852 | 75.52 |