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20162024
most citedSeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

632 citations · 2k across the 38 of their papers we have counts for

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Showing 2016Show all

8 papers · 1 filter

cs.SD2016★ 24 cited

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…

cs.SD2016★ 10 cited

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…

cs.NE2016

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…

cs.MM2016★ 33 cited

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…

cs.NE2016★ 12 cited

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…

cs.SD2016★ 5 cited

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…