Bayesian estimation of simultaneous musical notes based on frequency domain modelling

This paper proposes a Bayesian method for polyphonic music description. The method first divides an input audio signal into a series of sections called snapshots, and then estimates parameters such as fundamental frequencies and amplitudes of the notes contained in each snapshot. The parameter estimation process is based on a frequency domain modelling and Gibbs sampling. Experimental results obtained from audio signals of test note patterns are encouraging; the accuracy is better than 80% for the estimation of fundamental frequencies in terms of semitones and instrument names when the number of simultaneous notes is two.

A dynamic model to predict the occurrence of skidding in wind-turbine bearings

Despite use of the best in current design practices, high-speed shaft (HSS) bearings, in a wind-turbine gearbox, continue to exhibit a high rate of premature failure. As HSS bearings operate under low loads and high speeds, these bearings are prone to skidding. However, most of the existing methods for analyzing skidding are quasi-static in nature and cannot be used to study dynamic operating conditions. This paper proposes a dynamic model, which includes gyroscopic and centrifugal effects, to study the skidding characteristics of angular-contact ball-bearings. Traction forces between rolling-elements and raceways are obtained using elastohydrodynamic (EHD) lubrication theory. Underlying gross-sliding mechanisms for pure axial loads, and combined radial and axial loads are also studied. The proposed model will enable engineers to improve bearing reliability at the design stage, by estimating the amount of skidding. © 2011 Published under licence by IOP Publishing Ltd.