| Project/Area Number |
62550258
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
計算機工学
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| Research Institution | University of Tokyo |
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Principal Investigator |
MORIKAWA Hiroyoshi Faculty of Engineering, University of Tokyo, 工学部, 助手 (40011217)
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| Co-Investigator(Kenkyū-buntansha) |
HIROSE Keikichi Faculty of Engineering, University of Tokyo, 工学部, 助教授 (50111472)
FUJISAKI Hiroya Faculty of Engineering, University of Tokyo, 工学部, 教授 (80010776)
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| Project Period (FY) |
1987 – 1988
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| Project Status |
Completed (Fiscal Year 1988)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1988: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1987: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Noise Reduction / Speech Information Compression Coding System / Adaptive Kalman Filtering / Spectral Estimation / 有色雑音 / 音声情報圧縮符号化方式 |
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| Research Abstract |
A speech information compression coding is an effective speech transimission method for the speech band limited to relatively narrow frequency range such as modile stations and international telephone. Speech coding systems such as ADPCM, ATC and LPC have lately been out into practical use. However these systems are highly degraded by background noise. The purpose of the investigation is to establish the basis of a high quality speech transmission system in the noisy environments. The following results were obtained by improving the previous our works in the modeling of speech production process and noise generation process, adaptive Kalman filtering and speech synthesis system.
1. A method for speech/silence decision of speech corrupted by colored noise was proposed. This method is based on the properties of the short-time autocorrelation function of both speech and noise. After detecting the onset of speech activity, the adaptive spectrum estimation method was also developed to estimate both noise and speech spectrum.
2. The proposed methods were used for the speech signal uttered in the room noise generated by air conditioner and computer. The validity of the methods were confirmed from the point of view of spectral estimation accuracy for both synthetic and natural speech.
3. A method based on the adaptive Kalman filter was proposed to separate the speech signal from additive noise, in which the system parameters in the state-space representation of the speech production process were estimated adaptively.
4. From the experiments for both synthetic and natural speech, it was confirmed that the adaptive Kalman filtering has the effect of noise reduction for both voiced and unvoiced speech, but the effect is larger for voiced speech than for unvoiced speech. The validity of the proposed system was also demonstrated by objective and subjective evaluation tests of the reconstructed speech.
As ahown above, the initial goal of this study was almost accomplished.
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