Abstract
In this chapter, a problem of blind source separation for speech applications operated under real acoustic environments is addressed. In particular, we focus on a blind spatial subtraction array (BSSA) consisting of a noise estimator based on independent component analysis (ICA) for efficient speech enhancement. First, it is theoretically and experimentally pointed out that ICA is proficient in noise estimation rather than in speech estimation under a nonpoint-source noise condition. Next, motivated by the above-mentioned fact, we introduce a structure-generalized parametric BSSA, which consists of an ICA-based noise estimator and post-filtering based on generalized spectral subtraction. In addition, we perform its theoretical analysis via higher-order statistics. Comparing a parametric BSSA and a parametric channelwise BSSA, we reveal that a channelwise BSSA structure is recommended for listening but a conventional BSSA is more suitable for speech recognition.
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