632 citations · 2k across the 38 of their papers we have counts for
17 papers · 1 filter
ATGNN: Audio Tagging Graph Neural Network
Shubhr Singh, Christian J. Steinmetz, Emmanouil Benetos +2
Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers,…
A Deep Learning Architecture with Spatio-Temporal Focusing for Detecting Respiratory Anomalies
Dat Ngo, Lam Pham, Huy Phan +2
This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Our system initially performs audio feature extraction using Continuou…
An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies
Dat Ngo, Lam Pham, Huy Phan +3
This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Initially, our system begins with audio feature extraction using Gamma…
Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition
Jinhua Liang, Huy Phan, Emmanouil Benetos
Everyday sound recognition aims to infer types of sound events in audio streams. While many works succeeded in training models with high performance in a fully-supervised manner, t…
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…
Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases
Lam Pham, Huy Phan, Ross King +2
This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are fi…
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.…
Robust Acoustic Scene Classification using a Multi-Spectrogram Encoder-Decoder Framework
Lam Pham, Huy Phan, Truc Nguyen +3
This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature.…
Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases
Lam Pham, Ian McLoughlin, Huy Phan +3
This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves…
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…
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…
DNN and CNN with Weighted and Multi-task Loss Functions for Audio Event Detection
Huy Phan, Martin Krawczyk-Becker, Timo Gerkmann +1
This report presents our audio event detection system submitted for Task 2, "Detection of rare sound events", of DCASE 2017 challenge. The proposed system is based on convolutional…
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…
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…
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…