CNN-MoE based framework for classification of respiratory anomalies and lung disease detection

L Pham, H Phan, R Palaniappan… - IEEE journal of …, 2021 - ieeexplore.ieee.org
This paper presents and explores a robust deep learning framework for auscultation
analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from …

An analysis of state-of-the-art activation functions for supervised deep neural network

A Nguyen, K Pham, D Ngo, T Ngo… - … conference on system …, 2021 - ieeexplore.ieee.org
This paper provides an analysis of state-of-the-art activation functions with respect to
supervised classification of deep neural network. These activation functions comprise of …

A two-stage approach to device-robust acoustic scene classification

H Hu, CHH Yang, X Xia, X Bai, X Tang… - ICASSP 2021-2021 …, 2021 - ieeexplore.ieee.org
To improve device robustness, a highly desirable key feature of a competitive data-driven
acoustic scene classification (ASC) system, a novel two-stage system based on fully …

Acoustic scene classification using deep cnns with time-frequency representations

W Xie, Q He, H Yan, Y Li - 2021 IEEE 21st International …, 2021 - ieeexplore.ieee.org
The acoustic scene classification (ASC) problem is getting more and more attention. State-of-
the-art systems commonly utilize CNNs to learn high-level semantic information from the …

[图书][B] Robust deep learning frameworks for acoustic scene and respiratory sound classification

LD Pham - 2021 - search.proquest.com
Abstract Although research on Acoustic Scene Classification (ASC) is very close to, or even
overshadowed by different popular research areas known as Automatic Speech Recognition …

A Low-Compexity Deep Learning FrameworkFor Acoustic Scene Classification

L Pham, H Tang, A Jalal, A Schindler, R King - 2021 - osf.io
In this paper, we presents a low-complexitydeep learning frameworks for acoustic scene
classification (ASC). The proposed framework can be separated into threemain steps: Front …