Matrix analysis
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Matrix computations (3rd ed.)
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Numerical Methods with MATLAB: A Resource for Scientists and Engineers
Numerical Methods with MATLAB: A Resource for Scientists and Engineers
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
A Noise Reduction Method Based on Linear Prediction with Variable Step-Size
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
An adaptive noise canceller with low signal distortion for speechcodecs
IEEE Transactions on Signal Processing
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We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.