Výsledky vyhledávání - "Mazur, Radosław"

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

Externí odkaz: http://arxiv.org/abs/1703.04770

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What Makes Audio Event Detection Harder than Classification?

Autor: Phan, Huy, Koch, Philipp, Katzberg, Fabrice, Maass, Marco, Mazur, Radoslaw, McLoughlin, Ian, Mertins, Alfred

Publikováno v: Published in Proceedings of the 25th European Signal Processing Conference (EUSIPCO), pp. 2739-2743, 2017

There is a common observation that audio event classification is easier to deal with than detection. So far, this observation has been accepted as a fact and we lack of a careful analysis. In this paper, we reason the rationale behind this fact and,

Externí odkaz: http://arxiv.org/abs/1612.09089

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Measurement of Sound Fields Using Moving Microphones

Autor: Katzberg, Fabrice, Mazur, Radoslaw, Maass, Marco, Koch, Philipp, Mertins, Alfred

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 domain. Therefore, sampling inside a volume of interest requires

Externí odkaz: http://arxiv.org/abs/1609.09390

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Learning Compact Structural Representations for Audio Events Using Regressor Banks

Autor: Phan, Huy, Maass, Marco, Hertel, Lars, Mazur, Radoslaw, McLoughlin, Ian, Mertins, Alfred

We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using

Externí odkaz: http://arxiv.org/abs/1604.08716

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