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A New Bayesian Method Incorporating With Local Correlation for IBM Estimation

文献类型：期刊论文

作者Liang, Shan; Liu, Wenju; Jiang, Wei
刊名IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
出版日期2013-03-01
卷号21期号:3页码:476-487
关键词Bayesian rule computational auditory scene analysis (CASA) ideal binary mask (IBM) Markov Chain Monte Carlo (MCMC)
英文摘要A lot of efforts have been made in the Ideal Binary Mask (IBM) estimation via statistical learning methods. The Bayesian method is a common one. However, one drawback is that the mask is estimated for each time-frequency (T-F) unit independently. The correlation between units has not been fully taken into account. In this paper, we attempt to consider the local correlation information from two aspects to improve the performance. On one hand, a T-F segmentation based potential function is proposed to depict the local correlation between the mask labels of adjacent units directly. It is derived from a demonstrated assumption that units which belong to one segment are mainly dominated by one source. On the other hand, a local noise level tracking stage is incorporated. The local level is obtained by averaging among several adjacent units and can be considered as an approach to true noise energy. It is used as the intermediary auxiliary variable to indicate the correlation. While some secondary factors are omitted, the high dimensional posterior distribution is simulated by a Markov Chain Monte Carlo (MCMC) method. In iterations, the correlation is fully considered to compute the acceptance ratio. The estimate of IBM is obtained by the expectation. Our system is evaluated and compared with previous Bayesian system, and it yields substantially better performance in terms of HIT-FA rates and SNR gain.
WOS标题词Science & Technology ; Technology
类目[WOS]Acoustics ; Engineering, Electrical & Electronic
研究领域[WOS]Acoustics ; Engineering
关键词[WOS]VOICED SPEECH SEGREGATION ; SPECTRAL SUBTRACTION ; NOISE ; INTELLIGIBILITY ; RECOGNITION ; SEPARATION ; ALGORITHM
收录类别SCI
语种英语
WOS记录号WOS:000313425100002
源URL[http://ir.ia.ac.cn/handle/173211/2976]  
专题自动化研究所_模式识别国家重点实验室_机器人视觉团队
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liang, Shan,Liu, Wenju,Jiang, Wei. A New Bayesian Method Incorporating With Local Correlation for IBM Estimation[J]. IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2013,21(3):476-487.
APA Liang, Shan,Liu, Wenju,&Jiang, Wei.(2013).A New Bayesian Method Incorporating With Local Correlation for IBM Estimation.IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,21(3),476-487.
MLA Liang, Shan,et al."A New Bayesian Method Incorporating With Local Correlation for IBM Estimation".IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 21.3(2013):476-487.

入库方式： OAI收割

来源：自动化研究所

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