348 citations · 1.1k across the 16 of their papers we have counts for
21 papers
L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging
Huy Phan, Kristian P. Lorenzen, Elisabeth Heremans +7
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sle…
Polyphonic audio event detection: multi-label or multi-class multi-task classification problem?
Huy Phan, Thi Ngoc Tho Nguyen, Philipp Koch +1
Polyphonic events are the main error source of audio event detection (AED) systems. In deep-learning context, the most common approach to deal with event overlaps is to treat the A…
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
Huy Phan, Kaare Mikkelsen, Oliver Y. Chén +3
Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards inter…
Multi-view Audio and Music Classification
Huy Phan, Huy Le Nguyen, Oliver Y. Chén +4
We propose in this work a multi-view learning approach for audio and music classification. Considering four typical low-level representations (i.e. different views) commonly used f…
Self-Attention Generative Adversarial Network for Speech Enhancement
Huy Phan, Huy Le Nguyen, Oliver Y. Chén +4
Existing generative adversarial networks (GANs) for speech enhancement solely rely on the convolution operation, which may obscure temporal dependencies across the sequence input.…
On Multitask Loss Function for Audio Event Detection and Localization
Huy Phan, Lam Pham, Philipp Koch +3
Audio event localization and detection (SELD) have been commonly tackled using multitask models. Such a model usually consists of a multi-label event classification branch with sig…
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
Huy Phan, Oliver Y. Chén, Minh C. Tran +3
Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring…
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
Huy Phan, Kaare Mikkelsen, Oliver Y. Chén +4
Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteri…
Improving GANs for Speech Enhancement
Huy Phan, Ian V. McLoughlin, Lam Pham +4
Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use…
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
Huy Phan, Oliver Y. Chén, Philipp Koch +4
Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a…
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch
Huy Phan, Oliver Y. Chén, Philipp Koch +2
Many sleep studies suffer from the problem of insufficient data to fully utilize deep neural networks as different labs use different recordings set ups, leading to the need of tra…
Spatio-Temporal Attention Pooling for Audio Scene Classification
Huy Phan, Oliver Y. Chén, Lam Pham +4
Acoustic scenes are rich and redundant in their content. In this work, we present a spatio-temporal attention pooling layer coupled with a convolutional recurrent neural network to…
Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
Huy Phan, Oliver Y. Chén, Philipp Koch +4
Due to the variability in characteristics of audio scenes, some scenes can naturally be recognized earlier than others. In this work, rather than using equal-length snippets for al…
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
Huy Phan, Oliver Y. Chén, Philipp Koch +4
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages…
Enabling Early Audio Event Detection with Neural Networks
Huy Phan, Philipp Koch, Ian McLoughlin +1
This paper presents a methodology for early detection of audio events from audio streams. Early detection is the ability to infer an ongoing event during its initial stage. The pro…
Audio Scene Classification with Deep Recurrent Neural Networks
Huy Phan, Philipp Koch, Fabrice Katzberg +3
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-l…
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…