Ear-worn reference data collection and annotation for multimodal context-aware hearing instruments.

In this work we present a newly developed ear-worn sensing and annotation device to unobtrusively capture head movements in real life situations. It has been designed in the context of developing multimodal hearing instruments (HIs), but is not limited to this application domain. The ear-worn device captures triaxial acceleration, rate of turn and magnetic field and features a one-button-approach for real-time data annotation through the user. The system runtime is over 5 hours at a sampling rate of 128 Hz. In a user study with 21 participants the device was perceived as comfortable and showed a robust hold at the ear. On the example of head acceleration data we perform unsupervised clustering to demonstrate the benefit of head movements for multimodal HIs. We believe the novel technology will help to push the boundaries of HI technology.

An orthogonal family of quincunx wavelets with continuously adjustable order.

We present a new family of two-dimensional and three-dimensional orthogonal wavelets which uses quincunx sampling. The orthogonal refinement filters have a simple analytical expression in the Fourier domain as a function of the order lamda, which may be noninteger. We can also prove that they yield wavelet bases of L2(R2) for any lambda > 0. The wavelets are fractional in the sense that the approximation error at a given scale a decays like O(a(lamda)); they also essentially behave like fractional derivative operators. To make our construction practical, we propose a fast Fourier transform-based implementation that turns out to be surprisingly fast. In fact, our method is almost as efficient as the standard Mallat algorithm for separable wavelets.