Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A neural net for blind separation of nonstationary signals
Neural Networks
Blind identification of MIMO-FIR systems: a generalized linear prediction approach
Signal Processing - Special issue on blind source separation and multichannel deconvolution
Speech Coding and Synthesis
Subspace methods for multimicrophone speech dereverberation
EURASIP Journal on Applied Signal Processing
EVAM: an eigenvector-based algorithm for multichannel blinddeconvolution of input colored signals
IEEE Transactions on Signal Processing
Prediction error method for second-order blind identification
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Precise Dereverberation Using Multichannel Linear Prediction
IEEE Transactions on Audio, Speech, and Language Processing
Harmonicity-Based Blind Dereverberation for Single-Channel Speech Signals
IEEE Transactions on Audio, Speech, and Language Processing
Calculating Inverse Filters for Speech Dereverberation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Integrated speech enhancement method using noise suppression and dereverberation
IEEE Transactions on Audio, Speech, and Language Processing
Reverberant speech enhancement by temporal and spectral processing
IEEE Transactions on Audio, Speech, and Language Processing
Speech dereverberation based on variance-normalized delayed linear prediction
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
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This paper addresses the problem of blind speech dereverberation by inverse filtering of a room acoustic system. Since a speech signal can be modeled as being generated by a speech production system driven by an innovations process, a reverberant signal is the output of a composite system consisting of the speech production and room acoustic systems. Therefore, we need to extract only the part corresponding to the room acoustic system (or its inverse filter) from the composite system (or its inverse filter). The time-variant nature of the speech production system can be exploited for this purpose. In order to realize the time-variance-based inverse filter estimation, we introduce a joint estimation of the inverse filters of both the time-invariant room acoustic and the time-variant speech production systems, and present two estimation algorithms with distinct properties.