Steady-state and parameter tracking properties of self-tuning minimum variance regulators
Automatica (Journal of IFAC)
Fundamentals of speech recognition
Fundamentals of speech recognition
Identification of Time-Varying Processes
Identification of Time-Varying Processes
Linear Prediction of Speech
IEEE Transactions on Signal Processing
An efficient model-based multirate method for reconstruction of audio signals across long gaps
IEEE Transactions on Audio, Speech, and Language Processing
Analysis and Comparison of Multichannel Noise Reduction Methods in a Common Framework
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
On causal algorithms for speech enhancement
IEEE Transactions on Audio, Speech, and Language Processing
Proceedings of the second workshop on eHeritage and digital art preservation
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We present some results on audio restoration obtained with an algorithm that solves the problems of broadband noise filtering, signal parameters tracking, and impulsive noise removal by using the Extended Kalman Filter (EKF) theory. We show that, to achieve maximum performance, it is essential to optimize the EKF implementation. To this purpose, having to cope with the nonstationarity of the audio signal, we use two properly combined EKF filters (forward and backward), and introduce a bootstrapping procedure for model tracking. The careful combination of the proposed techniques and an accurate choice of some critical parameters, allows to improve the performance of the EKF algorithm. The presented procedure is validated by listening tests.