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Noise estimation and suppression using nonlinear function with a priori speech absence probability in speech enhancement

Title
Noise estimation and suppression using nonlinear function with a priori speech absence probability in speech enhancement
Author
이수정
Keywords
SPECTRAL DENSITY-ESTIMATION; MINIMUM STATISTICS; ESTIMATION ALGORITHM; ENVIRONMENTS
Issue Date
2016-05
Publisher
HINDAWI PUBLISHING CORP
Citation
JOURNAL OF SENSORS, n. 5352437, Page. 1-8
Abstract
This paper proposes a noise-biased compensation of minimum statistics (MS) method using a nonlinear function and a priori speech absence probability (SAP) for speech enhancement in highly nonstationary noisy environments. The MS method is a well-known technique for noise power estimation in nonstationary noisy environments; however, it tends to bias noise estimation below that of the true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function and a priori SAP for residual noise reduction. Additionally, our method uses an autoparameter to control the trade-off between speech distortion and residual noise. We evaluate the estimation of noise power in highly nonstationary and varying noise environments. The improvement can be confirmed in terms of signal-to-noise ratio (SNR) and the Itakura-Saito Distortion Measure (ISDM).
URI
https://www.hindawi.com/journals/js/2016/5352437/https://repository.hanyang.ac.kr/handle/20.500.11754/71407
ISSN
1687-725X; 1687-7268
DOI
10.1155/2016/5352437
Appears in Collections:
INDUSTRY-UNIVERSITY COOPERATION FOUNDATION[S](산학협력단) > ETC
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