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Benefits of noncircular statistics for nonstationary signals

Conventional statistical signal processing of nonstationary signals uses circular complex Gaussian distributions to model the complex-valued short-time Fourier transform. In this paper, we show how noncircular complex Gaussian distributions can provide better statistical models of a variety of nonstationary acoustic signals. The estimators required for this model are computationally efficient, and also have a simple approximate finite-sample distribution. We also show that noncircular Gaussian models provide distinct benefits for statistical signal processing. In particular, we show how noncircular Gaussian models can improve detection of nonstationary acoustic events, and we explore how estimator parameter choices affect performance.