no code implementations • 1 Mar 2024 • Tin Nguyen, Lam Pham, Phat Lam, Dat Ngo, Hieu Tang, Alexander Schindler
In this paper, we propose a deep learning based model for Acoustic Anomaly Detection of Machines, the task for detecting abnormal machines by analysing the machine sound.
no code implementations • 29 Jan 2024 • Phat Lam, Lam Pham, Tin Nguyen, Hieu Tang, Seidl Michael, Alexander Schindler
For this reason, the role of sentence embedding is crucial for capturing both the semantic information between words in the sentence and the contextual relationship of sentences within the abstract to provide a comprehensive representation for better classification.
no code implementations • 27 Dec 2023 • Cam Le, Lam Pham, Jasmin Lampert, Matthias Schlögl, Alexander Schindler
Finally, we propose a combined loss function which leverages Focal loss and IoU loss to train the network.
no code implementations • 17 Oct 2023 • Khoa Tran, Lam Pham, Hai-Canh Vu
To this end, we first propose and evaluate various machine learning based systems for the MBFD task.
no code implementations • 12 Sep 2023 • Khoa Tran, Hai-Canh Vu, Lam Pham, Nassim Boudaoud
In this paper, a Robust Multi-branch Deep learning-based system for remaining useful life (RUL) prediction and condition operations (CO) identification of rotating machines is proposed.
no code implementations • 16 May 2023 • Lam Pham, Dat Ngo, Cam Le, Anahid Jalali, Alexander Schindler
In the second phase, the student network, which presents a low complexity model, is trained with the embeddings extracted from the teacher.
no code implementations • 7 Mar 2023 • Dat Ngo, Lam Pham, Huy Phan, Minh Tran, Delaram Jarchi, Sefki Kolozali
Notably, we achieved the Top-1 performance in Task 2-1 and Task 2-2 with the highest Score of 74. 5% and 53. 9%, respectively.
no code implementations • 25 Feb 2023 • Lam Pham, Cam Le, Dat Ngo, Anh Nguyen, Jasmin Lampert, Alexander Schindler, Ian McLoughlin
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image.
no code implementations • 5 Nov 2022 • Cam Le, Lam Pham, Nghia NVN, Truong Nguyen, Le Hong Trang
In this paper, we present a robust and low complexity deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the scene of a remote sensing image.
no code implementations • 16 Oct 2022 • Lam Pham, Dusan Salovic, Anahid Jalali, Alexander Schindler, Khoa Tran, Canh Vu, Phu X. Nguyen
In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature.
no code implementations • 20 Jun 2022 • Lam Pham, Khoa Tran, Dat Ngo, Jasmin Lampert, Alexander Schindler
The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has taken the important role in a wide range of applications such as urban planning, natural hazards detection, environment monitoring, vegetation mapping, or geospatial object detection.
no code implementations • 13 Jun 2022 • Lam Pham, Dat Ngo, Anahid Jalali, Alexander Schindler
In this report, we presents low-complexity deep learning frameworks for acoustic scene classification (ASC).
no code implementations • 23 Mar 2022 • Lam Pham, Khoa Dinh, Dat Ngo, Hieu Tang, Alexander Schindler
In this paper, we present a robust and low complexity system for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording.
1 code implementation • 16 Dec 2021 • Lam Pham, Dat Ngo, Phu X. Nguyen, Truong Hoang, Alexander Schindler
This paper presents a task of audio-visual scene classification (SC) where input videos are classified into one of five real-life crowded scenes: 'Riot', 'Noise-Street', 'Firework-Event', 'Music-Event', and 'Sport-Atmosphere'.
no code implementations • 5 Apr 2021 • Anh Nguyen, Khoa Pham, Dat Ngo, Thanh Ngo, Lam Pham
This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network.
no code implementations • 3 Mar 2021 • Huy Phan, Huy Le Nguyen, Oliver Y. Chén, Lam Pham, Philipp Koch, Ian McLoughlin, Alfred Mertins
The learned embedding in the subnetworks are then concatenated to form the multi-view embedding for classification similar to a simple concatenation network.
no code implementations • 26 Dec 2020 • Dat Ngo, Lam Pham, Anh Nguyen, Ben Phan, Khoa Tran, Truong Nguyen
This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles.
no code implementations • 26 Dec 2020 • Lam Pham, Huy Phan, Ross King, Alfred Mertins, Ian McLoughlin
This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input.
no code implementations • 11 Sep 2020 • Huy Phan, Lam Pham, Philipp Koch, Ngoc Q. K. Duong, Ian McLoughlin, Alfred Mertins
Audio event localization and detection (SELD) have been commonly tackled using multitask models.
no code implementations • 4 Apr 2020 • Lam Pham, Huy Phan, Ramaswamy Palaniappan, Alfred Mertins, Ian McLoughlin
This paper presents and explores a robust deep learning framework for auscultation analysis.
no code implementations • 21 Jan 2020 • Lam Pham, Ian McLoughlin, Huy Phan, Minh Tran, Truc Nguyen, Ramaswamy Palaniappan
This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds.
2 code implementations • 15 Jan 2020 • Huy Phan, Ian V. McLoughlin, Lam Pham, Oliver Y. Chén, Philipp Koch, Maarten De Vos, Alfred Mertins
The former constrains the generators to learn a common mapping that is iteratively applied at all enhancement stages and results in a small model footprint.
no code implementations • 6 Apr 2019 • Huy Phan, Oliver Y. Chén, Lam Pham, Philipp Koch, Maarten De Vos, Ian McLoughlin, Alfred Mertins
Acoustic scenes are rich and redundant in their content.
no code implementations • 2 Nov 2018 • Huy Phan, Oliver Y. Chén, Philipp Koch, Lam Pham, Ian McLoughlin, Alfred Mertins, Maarten De Vos
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events.
no code implementations • 2 Nov 2018 • Huy Phan, Oliver Y. Chén, Philipp Koch, Lam Pham, Ian McLoughlin, Alfred Mertins, Maarten De Vos
Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task.