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The conventional soft-decision based noise estimation algorithms normally assume that noise exists, only when speech is absent. Consequently, the estimated noise spectra are not updated in the segments of speech presence, but only in those of speech absence. This assumption often results in several problems such as delay and bias of noise spectrum estimates. In this paper, we propose a solution by...
This paper presents a fully-pipelined CORDIC architecture for the simultaneous diagonalization of the covariance matrices. As an example, the problem of speech enhancement in a subspace based approach is considered, where in the covariance matrices of speech and noise are diagonalized concurrently. In order to compare the system performance of the proposed algorithm, objective measurements of speech...
We propose to use the features based on power spectral density as a descriptor of the EEG in the compression of the long-term intensive care unit EEG to obtain the temporal evolution of the recurrent patterns. Sleep EEG is used as a baseline since the sleep stages can be mapped to recurrent patterns in the background EEG. Our results indicate that the spectral features provide a better classification...
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