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HMM-Based Bandwidth Extension Using Baum-Welch Re-Estimation Algorithm  

Song, Geun-Bae (삼성전자)
Kim, Austin (삼성전자)
Publication Information
The Journal of the Acoustical Society of Korea / v.26, no.6, 2007 , pp. 259-268 More about this Journal
Abstract
This paper contributes to an improvement of the statistical bandwidth extension(BWE) system based on Hidden Markov Model(HMM). First, the existing HMM training method for BWE, which is suggested originally by Jax, is analyzed in comparison with the general Baum-Welch training method. Next, based on this analysis, a new HMM-based BWE method is suggested which adopts the Baum-Welch re-estimation algorithm instead of the Jax's to train HMM model. Conclusionally speaking, the Baum-Welch re-estimation algorithm is a generalized form of the Jax's training method. It is flexible and adaptive in modeling the statistical characteristic of training data. Therefore, it generates a better model to the training data, which results in an enhanced BWE system. According to experimental results, the new method performs much better than the Jax's BWE systemin all cases. Under the given test conditions, the RMS log spectral distortion(LSD) scores were improved ranged from 0.31dB to 0.8dB, and 0.52dB in average.
Keywords
Bandwidth extension; Hidden markov model; Gaussian mixture model; Baum-welch Re-estimation;
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