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Improvement of minimum tracking in Minimum Statistics noise estimation method
Hassan Farsi
Pages - 17 - 22 | Revised - 25-02-2010 | Published - 26-03-2010
Published in Signal Processing: An International Journal (SPIJ)
MORE INFORMATION
KEYWORDS
End point Detection, Distributed Speech Recognition , Voice activity detection , Zero Cross Rating , Robust speech recognition , Volume
ABSTRACT
Noise spectrum estimation is a fundamental component of speech enhancement and speech
recognition systems. In this paper we propose a new method for minimum tracking in Minimum
Statistics (MS) noise estimation method. This noise estimation algorithm is proposed for highly nonstationary
noise environments. This was confirmed with formal listening tests which indicated that the
proposed noise estimation algorithm when integrated in speech enhancement was preferred over
other noise estimation algorithms.
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| 3 | Yu Yao, & Zhao Heming. (2013). An improved minimum statistical noise power spectrum estimation algorithm. Computer Engineering and Applications, 49 (4). |
| 4 | Kallel, F., Ghorbel, M., Frikha, M., Berger-Vachon, C., & Hamida, A. B. (2012). A noise cross PSD estimator based on improved minimum statistics method for two-microphone speech enhancement dedicated to a bilateral cochlear implant. Applied Acoustics, 73(3), 256-264. |
| 5 | Yu Yao, & Zhao Heming. (2012). Noise power non-stationary noise environments spectral estimation method of data acquisition and processing, (4), 486-489. |
| 6 | Yu, Y., & Zhao, H. (2012). New noise estimation method for highly non-stationary noise environments. Journal of Data Acquisition & Processing, 27(4), 486-489. |
| 7 | Yu, Y., & Zhao, H. (2011, January). A new method for noise power spectrum estimation. In 4th IET International Conference on Wireless, Mobile & Multimedia Networks (ICWMMN 2011). |
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Dr. Hassan Farsi
University of Birjand - Iran
hfarsi@birjand.ac.ir
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