On tracking noise with linear dynamical system models

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 1, Issue , 2004, Pages

  Raj, Bhiksha ^a   Singh, Rita ^b   Stern, Richard ^b  

^a MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; FOURIER TRANSFORMS; KALMAN FILTERING; LINEAR SYSTEMS; MATHEMATICAL MODELS; SPEECH RECOGNITION; TIME DOMAIN ANALYSIS; VECTORS;

AUTOREGRESSIVE (AR) PROCESSES; EXTENDED KALMAN FILTERING (EKF); NOISE SPECTRA; NONSTATIONARY NOISE;

SPEECH PROCESSING;

EID: 4544365937     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Singh, R. and Raj, B. (2003), "Tracking noise via dynamical systems with a continuum of states," Proc. ICASSP 2003.
2. Varga, A.P. and Moore, R.K. (1990), "Hidden Markov model decomposition of speech and noise," Proc. ICASSP 1990, pp 845-548
3. Kim, N.S. (1998), "IMM-based estimation for slowly evolving environments," IEEE Signal Processing Letters, 5(6), 146-149
4. Moreno, P.J. (1996), Speech Recognition in Noisy Environments, Ph.D Thesis, ECE Department, Carnegie Mellon University
5. Gordon, N., Salmond, D. and Smith, A. (1993), "A novel approach to non-linear and non-Gaussian Bayesian state estimation," IEE Proceedings-F, vol. 140, pp. 107-113