Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Nguyen, Thi Ngoc Tho"'
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 AED task as a multi-label classification problem. By doing this, we inh
Externí odkaz:
http://arxiv.org/abs/2201.12557
Publikováno v:
Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 716-720
Polyphonic sound event localization and detection (SELD) has many practical applications in acoustic sensing and monitoring. However, the development of real-time SELD has been limited by the demanding computational requirement of most recent SELD sy
Externí odkaz:
http://arxiv.org/abs/2111.08192
Publikováno v:
Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 656-660
Echo and noise suppression is an integral part of a full-duplex communication system. Many recent acoustic echo cancellation (AEC) systems rely on a separate adaptive filtering module for linear echo suppression and a neural module for residual echo
Externí odkaz:
http://arxiv.org/abs/2110.00745
Autor:
Nguyen, Thi Ngoc Tho, Watcharasupat, Karn N., Nguyen, Ngoc Khanh, Jones, Douglas L., Gan, Woon-Seng
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 1749-1762, 2022
Sound event localization and detection (SELD) consists of two subtasks, which are sound event detection and direction-of-arrival estimation. While sound event detection mainly relies on time-frequency patterns to distinguish different sound classes,
Externí odkaz:
http://arxiv.org/abs/2110.00275
Autor:
Watcharasupat, Karn N., Nguyen, Thi Ngoc Tho, Nguyen, Ngoc Khanh, Lee, Zhen Jian, Jones, Douglas L., Gan, Woon Seng
The S{\o}rensen--Dice Coefficient has recently seen rising popularity as a loss function (also known as Dice loss) due to its robustness in tasks where the number of negative samples significantly exceeds that of positive samples, such as semantic se
Externí odkaz:
http://arxiv.org/abs/2107.10471
Autor:
Nguyen, Thi Ngoc Tho, Watcharasupat, Karn N., Lee, Zhen Jian, Nguyen, Ngoc Khanh, Jones, Douglas L., Gan, Woon Seng
Publikováno v:
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop, pp. 120-124
Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation. As a result, SELD inherits the challenges of both tasks, such as noise, reverberati
Externí odkaz:
http://arxiv.org/abs/2107.10469
Autor:
Nguyen, Thi Ngoc Tho, Watcharasupat, Karn, Nguyen, Ngoc Khanh, Jones, Douglas L., Gan, Woon Seng
Sound event localization and detection consists of two subtasks which are sound event detection and direction-of-arrival estimation. While sound event detection mainly relies on time-frequency patterns to distinguish different sound classes, directio
Externí odkaz:
http://arxiv.org/abs/2106.15190
Autor:
Liong, Venice Erin, Nguyen, Thi Ngoc Tho, Widjaja, Sergi, Sharma, Dhananjai, Chong, Zhuang Jie
In this paper, we present an Assertion-based Multi-View Fusion network (AMVNet) for LiDAR semantic segmentation which aggregates the semantic features of individual projection-based networks using late fusion. Given class scores from different projec
Externí odkaz:
http://arxiv.org/abs/2012.04934
Autor:
Nguyen, Thi Ngoc Tho, Nguyen, Ngoc Khanh, Phan, Huy, Pham, Lam, Ooi, Kenneth, Jones, Douglas L., Gan, Woon-Seng
Polyphonic sound event detection and localization (SELD) task is challenging because it is difficult to jointly optimize sound event detection (SED) and direction-of-arrival (DOA) estimation in the same network. We propose a general network architect
Externí odkaz:
http://arxiv.org/abs/2011.07859
Polyphonic sound event detection and direction-of-arrival estimation require different input features from audio signals. While sound event detection mainly relies on time-frequency patterns, direction-of-arrival estimation relies on magnitude or pha
Externí odkaz:
http://arxiv.org/abs/2002.05865