Wavelet - Based Statistical Signal Processing
نویسندگان
چکیده
Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coeecients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework based on wavelet-domain hidden Markov models (HMMs). The framework enables us to concisely model the statistical dependencies and nonGaussian statistics often encountered in practice. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful yet tractable proba-bilistic signal models. EEcient Expectation Maximization algorithms are developed for tting the HMMs to observational signal data. The new framework is suitable for a wide range of applications,
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تاریخ انتشار 1998