WEKO3
インデックスリンク
アイテム
Pitch extraction and voiced/unvoiced detection of speech by cross-coupling multi-layered neural network with feedback architecture
http://hdl.handle.net/2297/43970
http://hdl.handle.net/2297/43970f99bcfc1-8f8d-4c2d-899f-cc6157da00b4
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-FUNAADA-T-48-58.pdf (713.2 kB)
|
|
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-10-03 | |||||
| タイトル | ||||||
| タイトル | Pitch extraction and voiced/unvoiced detection of speech by cross-coupling multi-layered neural network with feedback architecture | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| 著者 |
Miyabayashi, Hideo
× Miyabayashi, Hideo× Funada, Tetsuo |
|||||
| 書誌情報 |
Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 巻 80, 号 9, p. 48-58, 発行日 1997-09-01 |
|||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1042-0967 | |||||
| NCID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA11330851 | |||||
| DOI | ||||||
| 関連タイプ | isIdenticalTo | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1002/(sici)1520-6440(199709)80:9<48::aid-ecjc6>3.0.co;2-w | |||||
| 出版者 | ||||||
| 出版者 | 電子情報通信学会 = (THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Pitch frequency is one of the most important voice characteristics, and its accurate extraction is important not only in speech analysis and synthesis, but also in speech coding, speech recognition, speaker recognition, and the like. Existing methods of improving extraction accuracy include waveform processing, correlative processing, and spectral processing. This paper describes the use of a neural network to extract pitch from voice features delivered from the bandpass filter pairs (BPFPs) proposed by Fonda et al. Three types of multi-layered neural networks able to learn time-continuity and high accuracy discrimination functions and have a recurrent structure are tested. The cross-coupling multi-layered neural network with feedback architecture gives the best improvement over conventional neural networks, and exhibits superior ability for learning time continuity of pitch and U/V information. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(9): 48–58, 1997. | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
| 関連URI | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.ieice.org/jpn/ | |||||
