完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Wu, BF | en_US |
dc.contributor.author | Wang, KC | en_US |
dc.date.accessioned | 2014-12-08T15:17:30Z | - |
dc.date.available | 2014-12-08T15:17:30Z | - |
dc.date.issued | 2006-02-01 | en_US |
dc.identifier.issn | 0916-8508 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1093/ietfec/e89-a.2.479 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/12685 | - |
dc.description.abstract | This study presents a fast adaptive algorithm for noise estimation in non-stationary environments. To make noise estimation adapt quickly to non-stationary noise environments, a robust entropy-based voice activity detection (VAD) is thus required. It is well-known that the entropy-based measure defined in spectral domain is very insensitive to the changing level of nose. To exploit the specific nature of straight lines existing on speech-only spectrogram, the proposed spectrum entropy measurement improved from spectrum entropy proposed by Shen et al. is further presented and is named band-splitting spectrum entropy (BSE). Consequently, the proposed recursive noise estimator including BSE-based VAD can update noise power spectrum accurately even if the noise-level quickly changes. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | noise measurement | en_US |
dc.subject | voice activity detection | en_US |
dc.subject | spectrum entropy | en_US |
dc.title | Noise spectrum estimation with entropy-based VAD in non-stationary environments | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1093/ietfec/e89-a.2.479 | en_US |
dc.identifier.journal | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | en_US |
dc.citation.volume | E89A | en_US |
dc.citation.issue | 2 | en_US |
dc.citation.spage | 479 | en_US |
dc.citation.epage | 485 | en_US |
dc.contributor.department | 交大名義發表 | zh_TW |
dc.contributor.department | National Chiao Tung University | en_US |
dc.identifier.wosnumber | WOS:000235508900018 | - |
dc.citation.woscount | 2 | - |
顯示於類別: | 期刊論文 |