1 code implementation • 5 Feb 2024 • Jisheng Bai, Mou Wang, Haohe Liu, Han Yin, Yafei Jia, Siwei Huang, Yutong Du, Dongzhe Zhang, Dongyuan Shi, Woon-Seng Gan, Mark D. Plumbley, Susanto Rahardja, Bin Xiang, Jianfeng Chen
In addition, considering the abundance of unlabeled acoustic scene data in the real world, it is important to study the possible ways to utilize these unlabelled data.
1 code implementation • 21 Nov 2023 • Jisheng Bai, Han Yin, Mou Wang, Dongyuan Shi, Woon-Seng Gan, Jianfeng Chen, Susanto Rahardja
This paper presents AudioLog, a large language models (LLMs)-powered audio logging system with hybrid token-semantic contrastive learning.
no code implementations • 3 Apr 2023 • Xu Tan, Jiawei Yang, Junqi Chen, Sylwan Rahardja, Susanto Rahardja
Experiments on 32 real-world OD datasets proved the effectiveness of the proposed methods.
no code implementations • 17 Mar 2023 • Jiawei Yang, Susanto Rahardja, Pasi Franti
To verify our proposed hypothesis, we propose an outlier score post-processing technique for outlier detectors, called neighborhood averaging(NA), which pays attention to objects and their neighbors and guarantees them to have more similar outlier scores than their original scores.
1 code implementation • 6 Sep 2022 • Ziyuan Xiao, Yina Han, Susanto Rahardja, Yuanliang Ma
Traditional statistic-based methods such as white balance and histogram stretching attempted to adjust the imbalance of color channels and narrow distribution of intensities a priori thus with limited performance.
1 code implementation • 11 Jun 2022 • Jie Chen, Min Zhao, Xiuheng Wang, Cédric Richard, Susanto Rahardja
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing.
no code implementations • 29 Nov 2020 • Wenbo Zhu, Mou Wang, Xiao-Lei Zhang, Susanto Rahardja
Among them, learnable features, which are trained with separation networks jointly in an end-to-end fashion, become a new trend of modern speech separation research, e. g. convolutional time domain audio separation network (Conv-Tasnet), while handcrafted and parameterized features are also shown competitive in very recent studies.
Sound
no code implementations • 30 Apr 2019 • Min Zhao, Mou Wang, Jie Chen, Susanto Rahardja
This paper presents an unsupervised nonlinear spectral unmixing method based on a deep autoencoder network that applies to a generalized linear-mixture/nonlinear fluctuation model, consisting of a linear mixture component and an additive nonlinear mixture component that depends on both endmembers and abundances.
no code implementations • 21 Dec 2017 • Yingxiang Sun, Jiajia Chen, Chau Yuen, Susanto Rahardja
It is known that adverse environments such as high reverberation and low signal-to-noise ratio (SNR) pose a great challenge to indoor sound source localization.
no code implementations • 21 Feb 2016 • Chencheng Li, Pan Zhou, Yingxue Zhou, Kaigui Bian, Tao Jiang, Susanto Rahardja
An increasing number of people participate in social networks and massive online social data are obtained.