| Project/Area Number |
20500158
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | University of the Ryukyus |
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Principal Investigator |
FUNAKI Keiichi University of the Ryukyus, 総合情報処理センター, 講師 (30315486)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2009: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2008: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 音声情報処理 / 信号処理 / 音声のF0推定 / 解析信号 / 複素音声分析 / ロバスト分析 / 音声符号化 / iLBC / 楽音の音高推定 / フォルマント推定 / 音声のFO推定 / G.711.1 / ALS / 音声のフォルマント推定 / 雑音に頑健 / 時変複素音声分析 |
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| Research Abstract |
F0 estimation of speech plays an important role on speech processing. One of the authors has proposed time-varying complex speech analysis for analytic speech signal and has proposed novel F0 estimation based on the time-varying complex speech analysis in which complex residual is estimated by the speech analysis and F0 is estimated by peak-picking the weighted auto-correlation with a reciprocal of the corresponding AMDF for the residual. It is called frame-based method. In this study, we propose more accurate and fast F0 estimation algorithm. In the method, pre-selections of F0 and F1 are realized by using F0 and F1 contour estimation method based on the time-varying complex analysis. It is called sample-based method. Final-selection of F0 is realized by the frame-based method with shorten range of F0 based on the estimated F0 and F1. The shorten range results in more accurate estimation with smaller computational amount in the two-stage F0 estimation. Furthermore, in order to investigate the efficacy of the time-varying analysis, the frame-based method is evaluated for each frame that is categorized into 4 modes with respect to the voiced strength. The experimental results confirm that the time-varying analysis can perform better for strong voiced frames.
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