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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gkn2pu46ozb4tmkxczacnmtvkq" style="color: black;">IEEE Transactions on Signal Processing</a>
For many signal sources such as speech with distinct, nonwhite power spectral densities, second-order statistics of the received signal mixture can be exploited for signal separation. Without knowledge on noise correlation matrix, we propose a simple and yet effective signal extraction method for signal source separation under unknown temporally white noise. This new and unbiased signal extractor is derived from the matrix pencil formed between output autocorrelation matrices at different<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/78.824690">doi:10.1109/78.824690</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z4us3473xze7jknom4h2uwxz4e">fatcat:z4us3473xze7jknom4h2uwxz4e</a> </span>
more »... . Based on the matrix pencil, an ESPRIT-type algorithm is derived to get an optimal solution in least square sense. Our method is well suited for systems with colored sensor noises and for nonstationary signals. Index Terms-Beamforming, blind source separation, cancellation, interference, nonstationary signal processing.
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