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This paper presents a novel set of critical band filterbanks, i.e. filters that mimic the human auditory system (HAS). The filterbanks are based on the empirical mode decomposition (EMD). Two cases are investigated: decimated and undecimated filters. Since the HAS does not follow conventional linear and stationary properties, non-uniform filterbanks approximating critical bands with EMD are developed. The EMD is a data-driven decomposition and, as such, is well suited to deal with nonlinear and nonstationary signals. Thus, it is natural that it is good fit for modeling the HAS both for speech and audio systems. As an application of the developed non-uniform filterbanks, noise removal is applied into each EMD critical band so that the auditory masking effect within the critical bands can be utilized in speech enhancement with the properties of EMD. The speech enhancement in the proposed EMD critical bands is compared in this paper with a speech enhancement algorithm that removes colored noises through simultaneous diagonalization of covariance matrices. Since the proposed filterbanks are very flexible in designing arbitrary tree structures, it is expected they can be used in various applications.

This paper introduces a new speech enhancement algorithm, which is efficient for color noises such as pink and babble noises. The algorithm applies the minimum variance estimator (MVE) into each intrinsic mode function (IMF) obtained by the empirical mode decomposition (EMD). Typical signal subspace approaches to speech enhancement assume the corrupting noise is white, thereby making a pre-whitening step mandatory when faced with various color noises. The algorithm presented in this paper is applicable into color noises, such as pink and babble noises, without the need to apply the whitening process. This is a result of the noises' variances in each IMF signal showing smaller fluctuations than without the application of the EMD. Experimental data demonstrates the effectiveness of the algorithm to combat color noises.

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