12 citations · 15 across the 2 of their papers we have counts for
4 papers
Deep Convolutional Neural Networks as Generic Feature Extractors
Lars Hertel, Erhardt Barth, Thomas Käster +1
Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve conv…
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling
Lars Hertel, Huy Phan, Alfred Mertins
We trained a deep all-convolutional neural network with masked global pooling to perform single-label classification for acoustic scene classification and multi-label classificatio…
Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks
Huy Phan, Lars Hertel, Marco Maass +1
We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with multipl…
Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning
Lars Hertel, Huy Phan, Alfred Mertins
Recognizing acoustic events is an intricate problem for a machine and an emerging field of research. Deep neural networks achieve convincing results and are currently the state-of-…