Fabrice Katzberg

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The sampling of sound fields involves the measurement of spatially dependent room impulse responses, where the Nyquist-Shannon sampling theorem applies in both the temporal and spatial domains. Therefore, sampling inside a volume of interest requires a huge number of sampling points in space, which comes along with further difficulties such as exact(More)
A multiresolution technique is presented for sound field recovery based on measurements of one or multiple moving microphones. The interpolation of the spatial samples enables us to set up a system of linear equations that recovers room impulse responses on a virtual uniform grid in space. The spacing of the virtual grid must be very small when directly(More)
Closed-room scenarios are characterized by reverberation, which decreases the performance of applications such as hands-free teleconferencing and multichannel sound reproduction. However, exact knowledge of the sound field inside a volume of interest enables the compensation of room effects and allows for a performance improvement within a wide range of(More)
We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks. An audio scene is firstly transformed into a sequence of high-level label tree embedding feature vectors. The vector sequence is then divided into multiple subsequences on which a deep GRUbased recurrent neural network is trained for(More)
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