Speech noise reduction by means of multi-signal minimum-cross-entropy spectral analysis

This paper presents results of a new spectrum-analysis method that estimates a number of power spectra when a prior estimate of each is available and new information is obtained in the form of values of the auto-correlation function of their sum. The method applies for instance when one obtains autocorrelation measurements for a signal with independent additive interference, and one has prior estimates of the signal and noise spectra. By incorporating prior estimates for both spectra, the method offers considerable flexibility for tailoring an estimator to the characteristics of a signal or noise. The new method, a generalization of Minimum-Cross-Entropy Spectrum Analysis (MCESA) and Maximum Entropy Spectrum Analysis (MESA), is called Multisignal MCESA. Its theoretical basis is reviewed, and results of experimental tests of an implementation are presented. The test data comprise digitized samples of speech corrupted with helicopter noise and tone interference.