Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix computations (3rd ed.)
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Time series: data analysis and theory
Time series: data analysis and theory
Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model
EURASIP Journal on Applied Signal Processing
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
Fast subspace tracking and neural network learning by a novelinformation criterion
IEEE Transactions on Signal Processing
Fast approximated power iteration subspace tracking
IEEE Transactions on Signal Processing - Part I
Voice activity detection based on multiple statistical models
IEEE Transactions on Signal Processing - Part I
Noise Tracking Using DFT Domain Subspace Decompositions
IEEE Transactions on Audio, Speech, and Language Processing
Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation
IEEE Transactions on Audio, Speech, and Language Processing
Minimum Mean-Square Error Estimation of Discrete Fourier Coefficients With Generalized Gamma Priors
IEEE Transactions on Audio, Speech, and Language Processing
Adaptive Time Segmentation for Improved Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
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Although most noise reduction algorithms are critically dependent on the noise power spectral density (PSD), most procedures for noise PSD estimation fail to obtain good estimates in nonstationary noise conditions. Recently, a DFT-subspace-based method was proposed which improves noise PSD estimation under these conditions. However, this approach is based on eigenvalue decompositions per DFT bin, and might be too computationally demanding for low-complexity applications like hearing aids. In this paper we present a noise tracking method with low complexity, but approximately similar noise tracking performance as the DFT-subspace approach. The presented method uses a periodogram with resolution that is higher than the spectral resolution used in the noise reduction algorithm itself. This increased resolution enables estimation of the noise PSD even when speech energy is present at the time-frequency point under consideration. This holds in particular for voiced type of speech sounds which can be modelled using a small number of complex exponentials.