Robust noise reduction for speech and audio signals

Statistical model-based methods are presented for the reconstruction of autocorrelated signals in impulsive plus continuous noise environments. Signals are modelled as autoregressive and noise sources as discrete and continuous mixtures of Gaussians, allowing for robustness in highly impulsive and non-Gaussian environments. Markov Chain Monte Carlo methods are used for reconstruction of the corrupted waveforms within a Bayesian probabilistic framework and results are presented for contaminated voice and audio signals.

Rotor - Stator broadband noise prediction

In this paper a semi analytic model for rotor - stator broadband noise is presented. The work can be split into two sections. The first examines the distortion of the rotor wake in mean swirling flow, downstream of the fan. Previous work by Cooper and Peake4 is extended to include dissipative effects. In the second section we consider the interaction of this gust with the downstream stator row. We examine the way in which an unsteady pressure field is generated by the interaction of this wake flow with the stator blades and obtain estimates for the radiated noise. A new method is presented to extend the well known LINSUB code to the third dimension to capture the effect of the spanwise wavenumber and stator lean and sweep. Copyright © 2008 by Adrian Lloyd and Nigel Peake.

Noise and stability in giant-chirp oscillators mode-locked with a nanotube-based saturable absorber

We compare experimental results showing stable dissipative-soliton solutions exist in mode-locked lasers with ultra-large normal dispersion (as large as 21.5 ps2), with both the analytic framework provided by Haus' master-equation and full numerical simulations. © 2010 Optical Society of America.