Comparison of music perception in bilateral and unilateral cochlear implant users and normal-hearing subjects

Music plays an important role in the daily life of cochlear implant (CI) users, but electrical hearing and speech processing pose challenges for enjoying music. Studies of unilateral CI (UCI) users' music perception have found that these subjects have little difficulty recognizing tempo and rhythm but great difficulty with pitch, interval and melody. The present study is an initial step towards understanding music perception in bilateral CI (BCI) users. The Munich Music Questionnaire was used to investigate music listening habits and enjoyment in 23 BCI users compared to 2 control groups: 23 UCI users and 23 normal-hearing (NH) listeners. Bilateral users appeared to have a number of advantages over unilateral users, though their enjoyment of music did not reach the level of NH listeners.

Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models

Several methods based on Kriging have recently been proposed for calculating a probability of failure involving costly-to-evaluate functions. A closely related problem is to estimate the set of inputs leading to a response exceeding a given threshold. Now, estimating such a level set—and not solely its volume—and quantifying uncertainties on it are not straightforward. Here we use notions from random set theory to obtain an estimate of the level set, together with a quantification of estimation uncertainty. We give explicit formulae in the Gaussian process set-up and provide a consistency result. We then illustrate how space-filling versus adaptive design strategies may sequentially reduce level set estimation uncertainty.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.