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Noisy speech enhancement based on improved minimum statistics incorporating acoustic environment-awareness

Title
Noisy speech enhancement based on improved minimum statistics incorporating acoustic environment-awareness
Author
장준혁
Keywords
Minimum statistics; Noise awareness; Gaussian mixture model
Issue Date
2013-07
Publisher
Elsevier Science B.V., Amsterdam
Citation
DIGITAL SIGNAL PROCESSING, 권: 23, 호: 4, 페이지: 1233-1238
Abstract
In this paper, we propose a novel speech enhancement technique based on an improved minimum statistics (MS) approach incorporating acoustic environmental noise awareness. A relevant noise estimation approach, known as MS, tracks the minimal values if a smoothed power estimate of the noisy signal is within a finite search window. From an investigation of previous MS-based methods, it is discovered that a fixed size of the minimum search window is assumed regardless of the environmental conditions. To overcome this limitation, we initially determine the optimal window sizes in terms of the perceived speech quality according to a variety of noise types. We then assign a different search window size according to the determined noise type, for which we use a real-time noise classification algorithm based on the Gaussian mixture model (GMM). The performance of the proposed approach is evaluated by a quantitative comparison method and by objective tests under various noise environments. It was found to yield better results compared to the previous MS method. (C) 2013 Elsevier Inc. All rights reserved.
URI
https://www.sciencedirect.com/science/article/pii/S1051200413000390?via%3Dihubhttp://hdl.handle.net/20.500.11754/44236
ISSN
1051-2004
DOI
10.1016/j.dsp.2013.02.016
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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