64 citations · 322 across the 45 of their papers we have counts for
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Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping
Khaled Koutini, Florian Henkel, Hamid Eghbal-zadeh +1
Deep Neural Networks are known to be very demanding in terms of computing and memory requirements. Due to the ever increasing use of embedded systems and mobile devices with a limi…
Learning to Read and Follow Music in Complete Score Sheet Images
Florian Henkel, Rainer Kelz, Gerhard Widmer
This paper addresses the task of score following in sheet music given as unprocessed images. While existing work either relies on OMR software to obtain a computer-readable score r…
On Data Augmentation and Adversarial Risk: An Empirical Analysis
Hamid Eghbal-zadeh, Khaled Koutini, Paul Primus +5
Data augmentation techniques have become standard practice in deep learning, as it has been shown to greatly improve the generalisation abilities of models. These techniques rely o…
Audio-Conditioned U-Net for Position Estimation in Full Sheet Images
Florian Henkel, Rainer Kelz, Gerhard Widmer
The goal of score following is to track a musical performance, usually in the form of audio, in a corresponding score representation. Established methods mainly rely on computer-re…
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
Khaled Koutini, Hamid Eghbal-zadeh, Matthias Dorfer +1
Convolutional Neural Networks (CNNs) have had great success in many machine vision as well as machine audition tasks. Many image recognition network architectures have consequently…
Mixture Density Generative Adversarial Networks
Hamid Eghbal-zadeh, Werner Zellinger, Gerhard Widmer
Generative Adversarial Networks have surprising ability for generating sharp and realistic images, though they are known to suffer from the so-called mode collapse problem. In this…