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Identification of virtual receiver array geometries that minimize audibility of numerical dispersion in binaural auralizations of finite difference time domain simulations 149

Identification of virtual receiver array geometries that min...

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149th Audio Engineering Society Convention 2020, AES 2020

作者： Meyer, Julie Lokki, Tapio Ahrens, Jens Aalto Acoustics Lab Department of Computer Science Aalto University Espoo Finland Aalto Acoustics Lab Department of Signal Processing and Acoustics Aalto University Espoo Finland Division of Applied Acoustics Chalmers University of Technology Gothenburg Sweden

This paper presents a perceptual evaluation of numerical dispersion in free-field headphone-based head-tracked binaural auralizations of finite difference time domain (FDTD) simulations. The simulated pressure, captured by virtual volumetric receiver arrays, is used to perform a spherical harmonics decomposition of the sound field and generate binaural signals. These binaural signals are compared perceptually to dispersion error-free binaural signals in a listening experiment designed using a duo-trio paradigm. The aim of the present work is to identify the size and density of the receiver array minimizing the audibility of numerical dispersion in the generated binaural signals. The spherical harmonics order was chosen to be 12 for the spatial decomposition. The overall reconstruction error, defined as the absolute value of the difference between the dispersion error-free and FDTD-simulated left-ear magnitude spectrum, was used as an objective metric to measure the spectral differences between the two signals. The listening experiment results show that this error does not correlate with the discrimination rates of the subjects. These results therefore suggest that this error does not suffice to describe the perceptual aspects introduced by numerical dispersion in the free-field dynamic binaural auralizations presented in the listening experiment. The results also show that increasing the receiver density for a fixed array size does not necessarily render numerical dispersion inaudible in the auralizations. Five out of 27 volumetric arrays led to FDTD-simulated binaural auralizations indistinguishable from the dispersion error-free binaural auralizations. © 2021 149th Audio Engineering Society Convention 2020, AES 2020. All Rights Reserved.

关键词： Errors

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

arXiv

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arXiv 2022年

作者： Zhao, Chenwei Ye, Miao Xue, Xingsi Lv, Jianhui Jiang, Qiuxiang Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Peng Cheng Lab. Guangdong Shenzhen518038 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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The minority matters: a diversity-promoting collab.rative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

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Proceedings of the 36th International Conference on Neural Information processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collab.rative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collab.rative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collab.rative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer

arXiv

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arXiv 2023年

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Kot, Alex C. The School of Information Technology Jiangxi University of Finance and Economics Nanchang330013 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Lab. School of Electrical and Electronic Engineering Nanyang Technology University Singapore The Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The China-Singapore International Joint Research Institute Singapore

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. One possible reason is that fully fine-tuned ViT-based models may disrupt the pre-trained features [1], [2] and overfit to some data-specific patterns [3]. To alleviate this issue, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. The code and trained model have been released at: https://***/LoveSiameseCat/FAViT. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code availab.e at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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A Deep Learning-Based Approach for Defect Detection and Removing on Archival Photos 18

A Deep Learning-Based Approach for Defect Detection and Remo...

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18th Image processing: Algorithms and Systems Conference, IPAS 2020

作者： Sizyakin, R. Voronin, V. Gapon, N. Zelensky, A. Pižurica, A. Department Telecommunications and Information Processing IPITELIN-imec Ghent University Ghent Belgium Moscow State University of Technology "STANKIN" Moscow Russia Lab. "Mathematical methods of image processing and intelligent computer vision systems" Don State Technical University Rostov-on-Don Russia

Many archival photos are unique, existed only in a single copy. Some of them are damaged due to improper archiving (e.g. affected by direct sunlight, humidity, insects, etc.) or have physical damage resulting in the appearance of cracks, scratches on photographs, non-necessary signs, spots, dust, and so on. This paper proposed a system for detection and removing image defects based on machine learning. The method for detecting damage to an image consists of two main steps: the first step is to use morphological filtering as a pre-processing, the second step is to use the machine learning method, which is necessary to classify pixels that have received a massive response in the preprocessing phase. The second part of the proposed method is based on the use of the adversarial convolutional neural network for the reconstruction of damages detected at the previous stage. The effectiveness of the proposed method in comparison with traditional methods of defects detection and removal was confirmed experimentally. © 2020, Society for Imaging Science and technology

关键词： Defects

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Hybrid-Field Channel Estimation for Massive MIMO Systems based on OMP Cascaded Convolutional Autoencoder

Hybrid-Field Channel Estimation for Massive MIMO Systems bas...

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IEEE Conference on Vehicular technology (VTC)

作者： Hasan Nayir Erhan Karakoca Ali Görçin Khalid Qaraqe Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electronics and Communication Engineering Istanbul Technical University İstanbul Turkey Communications and Signal Processing Research (HİSAR) Lab. TÜBİTAK BİLGEM Kocaeli Turkey Department of Electronics and Communication Engineering Yildiz Technical University İstanbul Turkey

ISBN: (纸本)9781665454698

Frequency scarcity implies the utilization of higher frequencies for wireless communications; however, spreading loss becomes a dominating issue as the frequency increases to the level of and beyond millimeter waves. To this end, massive multiple-input multiple-output structures introduce mitigation alternatives. However, to make these solutions possible, the channel estimation approach strives to be modified: since Rayleigh distance is very short for conventional systems, the only far-field channel is examined in that context. On the other hand, the implementation of massive antenna arrays in high frequencies increases Rayleigh distance; thus, both near-field and far-field analyses become necessary. Instead of a dual estimation process, it would be effective and efficient to develop hybrid-field channel estimation techniques. Therefore, in this study, a new channel estimation method which is based on convolutional autoencoder (CAE) and orthogonal matching pursuit (OMP) approach, is proposed for hybrid channel estimation. The results indicate that the proposed OMP-CAE method has much better error performance when compared to the conventional OMP algorithm, especially at low signal-to-noise ratio regimes.

关键词： Wireless communication Vehicular and wireless technologies Convolution Simulation Channel estimation Matching pursuit algorithms Ray tracing

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New Optimized Time Window Technique Based MFCC Feature Extraction for Text-Independent Speaker Verification

SSRN

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SSRN 2022年

作者： Yessad, Dalila Amrouche, Abderrahmane Faculty of Sciences and Technology Abdelhafid Boussouf University Mila43000 Algeria LASS Analysis of Signals and Systems Lab Faculty of Technology Mohamed Boudiaf University P.O. Box 166 Msila28000 Algeria LCPTS Speech Communication and Signal Processing Lab Faculty of Electronics and Computer Sciences USTHB P.O. Box 32 El Alia Bab Ezzouar Alger16111 Algeria

In this paper, we suggest a new method named Optimized Time Windowing (OTWin) technique aims to optimize features computation methods. We focus on the popular feature extractor Mel Frequency Cepstral Coefficients (MFCC) widely used in speech recognition and audio applications, in order to extract essential information from human voice which is by nature a nonstationary signal, to ensure its stationarity the audio signal is broken into short overlapping frames, windowed with a windowing function applied for each frame, to damp out the Gibbs phenomenon resulting from splitting signal. Our study investigate a new window technique OTWin as a step of MFCC feature extraction to improve speaker verification accuracy, versus MFCC using classical windows functions. The experimental results showed the effectiveness of our proposed method that achieved a high verification accuracy based novel OTWin Blackman window. This result confirm that MFCC based Efficient Optimized Time Windowing technique can be an alternative to the traditional MFCC for automatic speaker verification. © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

arXiv

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is availab.e at this url. © 2024, CC BY.

关键词： Semantics

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