Search Results for author: Susanto Rahardja

Found 10 papers, 4 papers with code

Neighborhood Averaging for Improving Outlier Detectors

no code implementations17 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.

USLN: A statistically guided lightweight network for underwater image enhancement via dual-statistic white balance and multi-color space stretch

1 code implementation6 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.

Image Enhancement

Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing

1 code implementation11 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.

Hyperspectral Unmixing

A comparison of handcrafted, parameterized, and learnable features for speech separation

no code implementations29 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

Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model

no code implementations30 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.

Hyperspectral Unmixing

Indoor Sound Source Localization with Probabilistic Neural Network

no code implementations21 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.

Direction of Arrival Estimation

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