16 citations · 26 across the 6 of their papers we have counts for
8 papers
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…
CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
Lam Pham, Huy Phan, Ramaswamy Palaniappan +2
This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect disease, from respirat…
Sound Context Classification Basing on Join Learning Model and Multi-Spectrogram Features
Dat Ngo, Hao Hoang, Anh Nguyen +2
In this paper, we present a deep learning framework applied for Acoustic Scene Classification (ASC), the task of classifying scene contexts from environmental input sounds. An ASC…
Deep Feature Embedding and Hierarchical Classification for Audio Scene Classification
Lam Pham, Ian McLoughlin, Huy Phan +2
In this work, we propose an approach that features deep feature embedding learning and hierarchical classification with triplet loss function for Acoustic Scene Classification (ASC…
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…