348 citations · 1.2k across the 23 of their papers we have counts for
9 papers · 1 filter
What Makes Audio Event Detection Harder than Classification?
Huy Phan, Philipp Koch, Fabrice Katzberg +4
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 a…
Measurement of Sound Fields Using Moving Microphones
Fabrice Katzberg, Radoslaw Mazur, Marco Maass +2
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 spa…
CaR-FOREST: Joint Classification-Regression Decision Forests for Overlapping Audio Event Detection
Huy Phan, Lars Hertel, Marco Maass +2
This report describes our submissions to Task2 and Task3 of the DCASE 2016 challenge. The systems aim at dealing with the detection of overlapping audio events in continuous stream…
CNN-LTE: a Class of 1-X Pooling Convolutional Neural Networks on Label Tree Embeddings for Audio Scene Recognition
Huy Phan, Lars Hertel, Marco Maass +2
We describe in this report our audio scene recognition system submitted to the DCASE 2016 challenge. Firstly, given the label set of the scenes, a label tree is automatically const…
Label Tree Embeddings for Acoustic Scene Classification
Huy Phan, Lars Hertel, Marco Maass +2
We present in this paper an efficient approach for acoustic scene classification by exploring the structure of class labels. Given a set of class labels, a category taxonomy is aut…
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…
Learning Compact Structural Representations for Audio Events Using Regressor Banks
Huy Phan, Marco Maass, Lars Hertel +3
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…
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-…