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Generalized Unitary Joint Diagonalization Algorithm Based on Approximate Givens Rotations

IEEE signal processing LETTERS 2024年 31卷 101-105页

作者： Azzouz, Malika Mesloub, Ammar Abed-Meraim, Karim Belouchrani, Adel Ecole Mil Polytech Lab Traitement Signal Algiers 16111 Algeria Univ Orleans F-45100 Orleans France Ecole Natl Polytech Algiers 16200 Algeria

Like the Joint Diagonalization of a unique set of matrices, Generalized Joint Diagonalization is an algebraic problem encountered in different applications such as data fusion and blind source separation. This letter proposes a new generalized unitary joint diagonalization approach based on the Jacobi iterative scheme using Givens rotations and simplified criterion, introducing three approximations. These approximations allowed us to reach a simultaneous estimation of different parameters. The first appears in the simplified criterion composed of entries doubly affected by the Givens rotations. The second approximation is in the Givens parameter using a small amplitude angle. The last approximation resides in keeping only the first order of transformed entries. Numerical experiments, including examples of joint blind audio source separation, are provided. The results show the effectiveness of the developed algorithm as compared to existing ones. The simultaneous estimation of different Givens rotations improves the algorithm's computation complexity and convergence rate.

关键词： Jacobian matrices Mathematical models Covariance matrices Approximation algorithms Convergence signal processing algorithms Estimation Generalized unitary joint diagonalization (GUJD) Givens rotations Jacobi iterative algorithm joint blind source separation (JBSS)

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Mixture to Mixture: Leveraging Close-Talk Mixtures as Weak-Supervision for Speech Separation

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IEEE signal processing LETTERS 2024年 31卷 1715-1719页

作者： Wang, Zhong-Qiu Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discriminative models to separate far-field mixtures. Our idea is that, for a target speaker, its close-talk mixture has a much higher signal-to-noise ratio (SNR) of the target speaker than any far-field mixtures, and hence could be utilized to design a weak supervision for separation. To realize this, at each training step we feed a far-field mixture to a deep neural network (DNN) to produce an intermediate estimate for each speaker, and, for each of considered close-talk and far-field microphones, we linearly filter the DNN estimates and optimize a loss so that the filtered estimates of all the speakers can sum up to the mixture captured by each of the considered microphones. Evaluation results on a 2-speaker separation task in simulated reverberant conditions show that M2M can effectively leverage close-talk mixtures as a weak supervision for separating far-field mixtures.

关键词： Microphones Machine-to-machine communications Training signal processing algorithms Microwave integrated circuits signal to noise ratio Indexes Weakly-supervised neural speech separation

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Optimal Parametric Estimation of Biased Sinusoidal signals Using DREM

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IEEE signal processing LETTERS 2024年 31卷 1049-1053页

作者： Gao, Dongxu Liu, Lijun Yu, Zhen Liu, Shihan Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Xiamen Univ Shenzhen Res Institude Shenzhen 518000 Peoples R China

This brief presents a novel optimal parametric estimation method of improving the reconstructing accuracy for biased sinusoidal signals. First, a second-order generalized integrator (SOGI) is used to construct a linear regression equation, whose unknown coefficient vector is a combination with the bias and frequency of the input signal. Next, the dynamic regression extension and mixing technique is employed to estimate the unknown parameters by solving this equation. Then, the particle swarm optimization algorithm simultaneously optimizes the parameters of SOGI and adaptive control gains. Finally, a comparison of the accuracy of frequency estimation and reconstruction ability illustrates the superiority of the proposed strategy.

关键词： Phase locked loops Frequency estimation Vectors Mathematical models Convergence signal reconstruction signal processing algorithms Adaptive control dynamic regressor extension and mixing frequency estimation parameter optimization

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Real-Time Light Field signal processing Using 4D/5D Linear Digital Filter FPGA Circuits

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第7期68卷 2735-2741页

作者： Edussooriya, C. U. S. Wijenayake, C. Madanayake, A. Liyanage, N. Premaratne, S. Vorhies, J. T. Dansereau, D. G. Agathoklis, P. Bruton, L. T. Univ Moratuwa Dept Elect & Telecommun Engn Moratuwa 10400 Sri Lanka Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld 4072 Australia Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada Univ Akron Dept Elect & Comp Engn Akron OH 44325 USA Univ Sydney Sydney Inst Robot & Intelligent Syst Camperdown NSW 2006 Australia Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

Light fields (LFs) and light field videos (LFVs) capture both angular and spatial variation of light rays emanating from scenes. This richness of information leads to novel applications such as post-capture refocusing, depth estimation and depth-velocity filtering which are not possible with images and videos. These capabilities come, however, with a significant increase in data to be processed. In order to fully exploit opportunities provided by LFs and LFVs, low-complexity signal processing algorithms that process LF and LFV data in real-time are required. In this brief, we survey such state-of-the-art algorithms, in particular for depth filtering, refocusing and denoising of LFs and depth-velcoty filtering for LFVs, and future directions for these real-time LF an LFV processing algorithms.

关键词： Real-time systems Finite impulse response filters Light fields Field programmable gate arrays Videos Manganese signal processing algorithms Light fields light field videos multi-dimensional filters real-time processing FPGA

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Compressive Sensing Total-Variation Primal-Dual algorithms for Image Reconstruction

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IEEE signal processing LETTERS 2024年 31卷 1965-1969页

作者： Pan, Zhongshan Liu, Zhaoting Luo, Kang Zhao, Yinan Xu, Xiaorong Hangzhou Dianzi Univ Dept Commun Engn Hangzhou 310018 Peoples R China

Image reconstruction remains a challenging task in image processing. In this letter, a compressive sensing total-variation primal-dual (CTPD) algorithm is developed for image reconstruction. The proposed CTPD algorithm involves minimization of a multi-term cost function with sparse and total-variation composite regularization. Instead of finding its optimal solution directly, this minimization problem is first cast into a convex-concave saddle point optimization problem, which can be solved by using a proximal primal-dual iteration algorithm with several simple sub-steps, each producing low complexity solvers. Moreover, in order to speed-up convergence, an accelerated version of the CTPD (A-CTPD) algorithm is also presented. In the A-CTPD algorithm, the primal-dual iteration is reformulated as a fixed-point iteration, which allows the use of an Anderson acceleration technique for faster convergence without sacrificing image reconstruction accuracy. Several numerical experiments demonstrate the efficiency of our proposed algorithms in comparison with other algorithms such as least squares (LS), the least absolute shrinkage and selection operator (LASSO), total variation (TV) image reconstruction, the alternating direction method of multipliers (ADMM), or fast iterative shrinkage-thresholding algorithm (FISTA).

关键词： Image reconstruction signal processing algorithms TV Compressed sensing Vectors Convex functions Convergence primal-dual iteration compressive sensing total-variation

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Synthetic Speech Detection Based on the Temporal Consistency of Speaker Features

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IEEE signal processing LETTERS 2024年 31卷 944-948页

作者： Zhang, Yuxiang Li, Zhuo Lu, Jingze Wang, Wenchao Zhang, Pengyuan Chinese Acad Sci Inst Acoust Key Lab Speech Acoust & Content Understanding Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Current synthetic speech detection (SSD) methods perform well on specific datasets but require improvement in interpretability and robustness. One possible reason is the lack of interpretability analysis of synthetic speech defects. In this paper, the flaws in the temporal consistency (TC) of speaker features inherent in the speech synthesis process are analyzed. Differences in the TC of intra-utterance speaker features arise due to limited control over speaker features during speech synthesis. The speech generated by text-to-speech algorithms exhibits higher TC, while the speech generated by voice conversion algorithms yields slightly lower TC compared to bonafide speech. Based on this finding, a new SSD method based on the TC of speaker features is proposed. Modeling the TC of intra-utterance speaker features extracted by a pre-trained ASV system can be used for SSD. The proposed method achieves equal error rates of 0.84%, 3.93%, 12.98% and 24.66% on the ASVspoof 2019 LA, 2021 LA, 2021 DF and IntheWild evaluation datasets, respectively, demonstrating strong interpretability and robustness.

关键词： Feature extraction Speech synthesis signal processing algorithms Training Robustness Partitioning algorithms Task analysis Anti-spoofing interpretability pre-trained system robustness speaker verification

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Symbol Detection for Coarsely Quantized OTFS

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IEEE signal processing LETTERS 2024年 31卷 461-465页

作者： He, Junwei Zhang, Haochuan Dong, Chao Zhu, Huimin Guangdong Univ Technol Sch Automat Guangdong Hong Kong & Macao Smart Discrete Mfg Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Technol Sch Automat Key Lab Guangdong Prov Informat Technol Internet T Guangzhou 510006 Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Guangzhou Univ Chinese Med Res Ctr Basic Integrat Med Sch Basic Med Sci Dept Physiol Guangzhou 510006 Peoples R China

This letter explicitly models a coarse and noisy quantization in a communication system empowered by orthogonal time frequency space (OTFS) for cost and power efficiency. We first point out, with coarse quantization, the effective channel is imbalanced and thus no longer able to circularly shift the transmitted symbols along the delay-Doppler domain. Meanwhile, the effective channel is non-isotropic, which imposes a significant loss to symbol detection algorithms like the original approximate message passing. Although the algorithm of generalized expectation consistent for signal recovery (GEC-SR) can mitigate this loss, the complexity in computation is prohibitively high, mainly due to an dramatic increase in the matrix size of OTFS. In this context, we propose a low-complexity algorithm that embed into GEC-SR a quick inversion of the quasi-banded matrices, thus reducing the algorithm's complexity from cubic order to linear order, while keeping the performance at almost the same level.

关键词： Quantization (signal) Symbols Manganese Complexity theory Transforms signal processing algorithms Message passing OTFS coarse quantization GEC-SR matrix inversion low-complexity

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Single-BS Simultaneous Environment Sensing and UE Localization Without LoS Path by Exploiting Near-Field Scatterers

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IEEE COMMUNICATIONS LETTERS 2024年第9期28卷 2071-2075页

作者： Zhou, Zhiwen Xiao, Zhiqiang Zeng, Yong Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

As the mobile communication network evolves over the past few decades, localizing user equipment (UE) has become an important network service. While localization in line-of-sight (LoS) scenarios has reached a level of maturity, it is known that in far-field scenarios without a LoS path nor any prior information about the scatterers, accurately localizing the UE is impossible. In this letter, we show that this becomes possible if there are scatterers in the near-field region of the base station (BS) antenna arrays. Specifically, by exploiting the additional distance sensing capability of extremely large-scale antenna arrays (XL-arrays) provided by near-field effects, we propose a novel method that simultaneously performs environment sensing and non-line-of-sight (NLoS) UE localization using one single BS. In the proposed method, the BS leverages the near-field characteristics of XL-arrays to directly estimate the locations of the near-field scatterers with array signal processing, which then serves as virtual anchors for UE localization. Then, the propagation delay for each path is estimated and the position of the UE is obtained based on the positions of scatterers and the path delays. Simulation results demonstrate that the proposed method achieves superior accuracy and robustness with similar complexity compared with benchmark methods.

关键词： Location awareness Sensors signal processing algorithms OFDM Estimation Delays Clocks Near-field localization NLoS MUSIC

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Automatic Tuning of Denoising algorithms Parameters Without Ground Truth

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IEEE signal processing LETTERS 2024年 31卷 381-385页

作者： Floquet, Arthur Dutta, Sayantan Soubies, Emmanuel Pham, Duong-Hung Kouame, Denis Univ Toulouse IRIT Lab F-31400 Toulouse France CNRS F-31400 Toulouse France Weill Cornell Med Dept Radiol New York NY 10022 USA

Denoising is omnipresent in image processing. It is usually addressed with algorithms relying on a set of hyperparameters that control the quality of the recovered image. Manual tuning of those parameters can be a daunting task, which calls for the development of automatic tuning methods. Given a denoising algorithm, the best set of parameters is the one that minimizes the error between denoised and ground-truth images. Clearly, this ideal approach is unrealistic, as the ground-truth images are unknown in practice. In this work, we propose unsupervised cost functions-i.e., that only require the noisy image-that allow us to reach this ideal gold standard performance. Specifically, the proposed approach makes it possible to obtain an average PSNR output within less than 1% of the best achievable PSNR.

关键词： Noise measurement Noise reduction Tuning Training signal processing algorithms Costs Noise level Bilevel optimization denoising hyper-parameter tuning

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A Third-Order Majorization Algorithm for Logistic Regression With Convergence Rate Guarantees

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IEEE signal processing LETTERS 2024年 31卷 1700-1704页

作者： Zhao, Licheng Pu, Wenqiang Zhou, Rui Shi, Qingjiang Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

In this paper, we study the classical Logistic Regression (LR) problem in machine learning. Traditionally, the solving algorithms are based on either the first- or second-order approximation of the objective. For instance, the Fixed-Hessian Newton (FHN) method approximates the true Hessian with a constant estimate. In contrast, our design additionally exploits the third-order information. Applying the majorization-minimization (MM) framework, we construct a novel majorizing function based on the third-order Taylor expansion and the minimization solution is in closed-form with perseverance of the true gradient and Hessian structures. In analysis, we prove the convergence rate of the proposed algorithm. The enhanced numerical performance can be verified through simulation results.

关键词： signal processing algorithms Minimization Convergence Optimization Taylor series Machine learning algorithms Logistic regression Convergence rate logistic regression majorization-minimization third-order

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