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An Adaptive Orthogonal M-Split Initialization Method for VQ Codebook Generation
Weijun He,Qianhua He,Jichen Yang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8
Linde–Buzo–Gray (LBG) algorithm is a universal method to design codebook in vector quantization(VQ). This paper proposed an adaptive orthogonal M-split initialization method to improve the computational efficiency of LBG algorithm. The method splits one code word into 2, 4 or 5 new code words with adaptive split coefficient vectors and set the increment to be orthogonal in 4-split and 5-split situations, aiming at decreasing the iterations of the following clustering. Experiment is conducted on both TIMIT and RASC863 speech database, which shows that the proposed algorithm provides a reduction of 18%~45% in designing codebook in size of 64~2048 with almost equal VQ performance, compared with the universal codebook generation algorithm.
Estimating Key Speaker in Meeting Speech Based on Multiple Features Optimization
Wei Li Yanxiong Li,Qianhua He 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.4
This paper proposes to estimate key speaker in meeting speech based on multiple features optimization. First, each feature is defined and their differences between key speaker and other speakers are analyzed. Then, a decision function of multiple feature weighting is generated for estimating key speaker in meeting speech, and the genetic algorithm is used to optimize these coefficients of feature weighting. The methods are evaluated on three different meeting speech datasets. Experimental results show that the proposed optimization method obtains average accuracy of 93.3% for estimating key speaker, and gains average accuracy improvement by 9.7% and 4.1% compared with the previous method and the feature weighting method without optimization, respectively.