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Stable Extended U-Net for Noise-Robust Speaker Verification

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Stable Extended U-Net for Noise-Robust Speaker Verification

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zonghui Wang Zhihua Fang Liang He School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Intelligence Science and Technology Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. Researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model’s performance improvement. Research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through subpixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness.

关键词： Training Correlation Attention mechanisms Convolution Data models Stability analysis Robustness Noise measurement Speech processing signal to noise ratio

An Improved Differential Evolution Algorithm Integrating the Plant Evolutionary Strategy

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SSRN

SSRN 2024年

作者： Zhang, Xiaoming Cheng, Hongwei Li, Jun Li, Tingjuan Zhang, Panpan Tian, Ye Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Qinghai Institute of Science and Technology Information Xining810008 China School of Computer Science and Technology Anhui University Hefei230601 China

Differential evolution algorithm (DE) and their variants often encounter a decrease in population diversity and convergence rate along with the evolution, resulting in local optima and premature convergence. To address this issue, this paper proposes an improved differential evolution integrating the plant evolutionary strategy, named DEPES, leveraging the exploitation capability of DE and the exploration capability of the plant population distribution evolutionary strategy. First, a tri-subpopulation optimization framework with a mutation strategy is introduced to cope with diversity maintenance, convergence enhancement, and local optima a Cauchy mutation operator based on plant population distribution evolutionary model is employed to enhance the exploration capability of the mutation strategy. Second, a parameter control strategy is performed on the superior and inferior solutions to balance the exploitation and exploration of the population. Third, a covariance matrix-based crossover strategy is designed to minimize interference from local population information, consequently generating high-quality solutions. The superior efficiency of DEPES is fully verified by comparing it with advanced DE-based variants and other algorithms on the IEEE CEC2020 test suite and engineering design problems. © 2024, The Authors. All rights reserved.

关键词： Covariance matrix

Determining the covering radius of all generalized Zetterberg codes in odd characteristic

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arXiv 2024年

作者： Shi, Minjia Li, Shitao Helleseth, Tor Özbudak, Ferruh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China School of Mathematical Sciences Anhui University Anhui Hefei230601 China Department of Informatics University of Bergen Bergen Norway Faculty of Engineering and Natural Sciences Sabancı University Istanbul34956 Turkey

For an integer s ≥ 1, let Cs(q0) be the generalized Zetterberg code of length qs0 + 1 over the finite field Fq0 of odd characteristic. Recently, Shi, Helleseth, and Özbudak (IEEE Trans. Inf. Theory 69(11): 7025-7048, 2023) determined the covering radius of Cs(q0) for (formula presented) (mod 8), and left the remaining case as an open problem. In this paper, we develop a general technique involving arithmetic of finite fields and algebraic curves over finite fields to determine the covering radius of all generalized Zetterberg codes for (formula presented) (mod 8), which therefore solves this open problem. We also introduce the concept of twisted half generalized Zetterberg codes of length (formula presented) , and show the same results hold for them. As a result, we obtain some quasi-perfect codes. Copyright © 2024, The Authors. All rights reserved.

关键词： Turbo codes

Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bound States in the Continuum

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Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bou...

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yuhang Liu Xingang Ren Kaikun Niu Zhixiang Huang School of Electronic and Information Engineer Anhui University Hefei China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Chizhou Anhui Province China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

Bound state in the continuum (BIC) is a dark mode with infinitely high quality factors(Q-factors), it has recently attracted considerable research attention in photonics. Here, we present a metasurface that can support two BICs, which consists of AlGaAs. By breaking the structural symmetry, the two BICs are transformed into quasi-bic modes because they are not protected by the symmetry. Furthermore, it is found that the two quasi-BICs resonate near the fundamental and the second harmonic respectively, which can be used to enhance efficiency of second harmonic generation. By exploiting the high Q characteristics of this structure at both frequencies, we can obtain a second harmonic generation efficiency of nearly 10– 3 with 1GW/cm 2 of pump intensity. Our proposed metasurface will offer good prospects for nonlinear operation.

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An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition

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arXiv 2024年

作者： Xia, Yizhang Song, Shihao Hou, Zhanglu Xu, Junwen Zou, Juan Liu, Yuan Yang, Shengxiang Hunan Engineering Research Center of Intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China The Centre for Computational Intelligence School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Hand gesture recognition (HGR) based on multimodal data has attracted considerable attention owing to its great potential in applications. Various manually designed multimodal deep networks have performed well in multimodal HGR (MHGR), but most of existing algorithms require a lot of expert experience and time-consuming manual trials. To address these issues, we propose an evolutionary network architecture search framework with the adaptive multimodel fusion (AMF-ENAS). Specifically, we design an encoding space that simultaneously considers fusion positions and ratios of the multimodal data, allowing for the automatic construction of multimodal networks with different architectures through decoding. Additionally, we consider three input streams corresponding to intra-modal surface electromyography (sEMG), intra-modal accelerometer (ACC), and inter-modal sEMG-ACC. To automatically adapt to various datasets, the ENAS framework is designed to automatically search a MHGR network with appropriate fusion positions and ratios. To the best of our knowledge, this is the first time that ENAS has been utilized in MHGR to tackle issues related to the fusion position and ratio of multimodal data. Experimental results demonstrate that AMF-ENAS achieves state-of-the-art performance on the Ninapro DB2, DB3, and DB7 datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

Study of Robust Direction Finding Based on Joint Sparse Representation

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arXiv 2024年

作者： Li, Yingsong Xiao, Wudang Zhao, Luyu Huang, Zhixiang Li, Qiang Li, Liping de Lamare, Rodrigo C. The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei230601 China PUC-Rio Brazil The University of York United Kingdom Gávea 22451-900 Brazil The Department of Electronic Engineering University of York YorkYO10 5DD United Kingdom

Standard Direction of Arrival (DOA) estimation methods are typically derived based on the Gaussian noise assumption, making them highly sensitive to outliers. Therefore, in the presence of impulsive noise, the performance of these methods may significantly deteriorate. In this paper, we model impulsive noise as Gaussian noise mixed with sparse outliers. By exploiting their statistical differences, we propose a novel DOA estimation method based on sparse signal recovery (SSR). Furthermore, to address the issue of grid mismatch, we utilize an alternating optimization approach that relies on the estimated outlier matrix and the on-grid DOA estimates to obtain the off-grid DOA estimates. Simulation results demonstrate that the proposed method exhibits robustness against large outliers. Copyright © 2024, The Authors. All rights reserved.

关键词： Impulse noise

Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking

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arXiv 2024年

作者： Zhang, Jiyao Huang, Weiyao Peng, Bo Wu, Mingdong Hu, Fei Chen, Zijian Zhao, Bo Dong, Hao Center on Frontiers of Computing Studies School of Computer Science Peking University China PKU-Agibot Lab School of Computer Science Peking University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Waseda University Japan Beijing Academy of Artificial Intelligence China

6D Object Pose Estimation is a crucial yet challenging task in computer vision, suffering from a significant lack of large-scale datasets. This scarcity impedes comprehensive evaluation of model performance, limiting research advancements. Furthermore, the restricted number of available instances or categories curtails its applications. To address these issues, this paper introduces Omni6DPose, a substantial dataset characterized by its diversity in object categories, large scale, and variety in object materials. Omni6DPose is divided into three main components: ROPE (Real 6D Object Pose Estimation Dataset), which includes 332K images annotated with over 1.5M annotations across 581 instances in 149 categories;SOPE(Simulated 6D Object Pose Estimation Dataset), consisting of 475K images created in a mixed reality setting with depth simulation, annotated with over 5M annotations across 4162 instances in the same 149 categories;and the manually aligned real scanned objects used in both ROPE and SOPE. Omni6DPose is inherently challenging due to the substantial variations and ambiguities. To address this challenge, we introduce GenPose++, an enhanced version of the SOTA category-level pose estimation framework, incorporating two pivotal improvements: Semantic-aware feature extraction and Clustering-based aggregation. Moreover, we provide a comprehensive benchmarking analysis to evaluate the performance of previous methods on this large-scale dataset in the realms of 6D object pose estimation and pose tracking. Additional demonstrations are available at https://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Adversarial Learning Network for Recovering Rppg signals from Low-Resolution Images for Remote Heart Rate Measurement

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SSRN

SSRN 2024年

作者： Zhai, Dezhao Chen, Wei Li, Qinwei Yu, Ming Wu, Hang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin300300 China School of Artificial Intelligence Nankai University Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, which has significant research value and application potential. However, in long-distance vital sign measurement tasks, the low resolution of facial images and insufficient pixels in the facial region severely degrade the quality of the rPPG signals, leading to a sharp decline in HR accuracy. To address this issue, this paper proposes an adversarial learning network for recovering rPPG signals from low-resolution images (ALRSL-HR). First, arbitrary resolution face images are fed into multi-channel network to extract rPPG signal features capturing rich rPPG information, generating rPPG feature blocks, and temporally aggregating for richer feature representation. These features are then inputted into the backbone network to generate adversarial examples of rPPG signals of varying qualities. Finally, a two-stage adversarial loss function is employed to enable the low-quality rPPG signals to learn the periodic characteristics of high-quality rPPG signals, recover the low-quality signals, and narrow the quality gap between rPPG signals from low-resolution images. Extensive experiments were conducted in this paper to validate the effectiveness of our model on the public COHFACE, UBFC-Phys and UBFC-rPPG datasets. The results of the experiments demonstrate that the proposed ALRSL-HR model can effectively recover degraded rPPG signals from low-resolution images. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision