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has been cited by the following article:

• TITLE: Sudden Noise Reduction Based on GMM with Noise Power Estimation

  AUTHORS: Nobuyuki Miyake, Tetsuya Takiguchi, Yasuo Ariki

  KEYWORDS: Sudden Noise, Model-Based Noise Reduction, Speech Recognition

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.3 No.4, May 6, 2010

  ABSTRACT: This paper describes a method for reducing sudden noise using noise detection and classification methods, and noise power estimation. Sudden noise detection and classification have been dealt with in our previous study. In this paper, GMM-based noise reduction is performed using the detection and classification results. As a result of classification, we can determine the kind of noise we are dealing with, but the power is unknown. In this paper, this problem is solved by combining an estimation of noise power with the noise reduction method. In our experiments, the proposed method achieved good performance for recognition of utterances overlapped by sudden noises.