Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises.In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain.We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.
Wet compression moulding (WCM) provides high-volume production potential for continuous fibre-reinforced composite components via simultaneous draping and infiltration. Experimental and theoretical investigations proved strong mutual dependencies between resin flow and fabric deformation, which are not fully understood yet. This limits development of suitable process simulation methods and applies in particular for the characterisation of infiltrated bending behaviour-essential for an accurate prediction of draping effects. Therefore, a comparative characterisation of the bending behaviour of dry and infiltrated woven fabrics is presented using a modified cantilever and a rheometer bending test. Experimental results reveal both, rate-and viscosity-dependencies. A comparison of the quantitative results exposed an explicable systematic deviation between the two tests, whereas qualitative results are comparable. Finally, Finite Element forming simulations, comprising two bending models corresponding to cantilever and rheometer test are performed to evaluate the experimental findings on component level.
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