103 citations · 305 across the 11 of their papers we have counts for
11 papers
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift
Jisheng Bai, Mou Wang, Haohe Liu +11
Acoustic scene classification (ASC) is a crucial research problem in computational auditory scene analysis, and it aims to recognize the unique acoustic characteristics of an envir…
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning
Jisheng Bai, Han Yin, Mou Wang +4
Previous studies in automated audio captioning have faced difficulties in accurately capturing the complete temporal details of acoustic scenes and events within long audio sequenc…
Neighborhood Averaging for Improving Outlier Detectors
Jiawei Yang, Susanto Rahardja, Pasi Franti
We hypothesize that similar objects should have similar outlier scores. To our knowledge, all existing outlier detectors calculate the outlier score for each object independently r…
USLN: A statistically guided lightweight network for underwater image enhancement via dual-statistic white balance and multi-color space stretch
Ziyuan Xiao, Yina Han, Susanto Rahardja +1
Underwater images are inevitably affected by color distortion and reduced contrast. Traditional statistic-based methods such as white balance and histogram stretching attempted to…
Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition
Maboud F. Kaloorazi, Kai Liu, Jie Chen +2
The pivoted QLP decomposition is computed through two consecutive pivoted QR decompositions, and provides an approximation to the singular value decomposition. This work is concern…
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
Jie Chen, Min Zhao, Xiuheng Wang +2
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpreta…
Sparse Linear Spectral Unmixing of Hyperspectral images using Expectation-Propagation
Zeng Li, Yoann Altmann, Jie Chen +2
This paper presents a novel Bayesian approach for hyperspectral image unmixing. The observed pixels are modeled by a linear combination of material signatures weighted by their cor…
A comparison of handcrafted, parameterized, and learnable features for speech separation
Wenbo Zhu, Mou Wang, Xiao-Lei Zhang +1
The design of acoustic features is important for speech separation. It can be roughly categorized into three classes: handcrafted, parameterized, and learnable features. Among them…
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model
Min Zhao, Mou Wang, Jie Chen +1
Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed individual pixels. Recently…
Indoor Sound Source Localization with Probabilistic Neural Network
Yingxiang Sun, Jiajia Chen, Chau Yuen +1
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. To address this cha…
Distributed Private Online Learning for Social Big Data Computing over Data Center Networks
Chencheng Li, Pan Zhou, Yingxue Zhou +3
With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massiv…