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Optimal Speech signal processing Based on Dynamic Multi-Scale Analysis

IEEE ACCESS 2025年 13卷 22946-22955页

作者： Guo, Zefei Li, Xiao Zhang, Yuchen Tibet Univ Coll Informat Sci & Technol Lhasa 850000 Peoples R China

To enhance the quality of speech signals, this paper introduces a novel speech signal processing method that integrates Dynamic Multi-Scale (DMS) and Adaptive Error Minimization (AEM) techniques. This method significantly enhances noise reduction and signal fidelity in dynamic environments, distinguishing itself from previous approaches through its real-time adaptive filtering, which makes it highly adaptable to complex, non-stationary noise conditions. The proposed method is grounded in dynamic multi-scale analysis, employing multi-scale decomposition of speech signals to optimize their time-frequency characteristics and dynamic adjustments, thereby forming a new noise reduction approach, DMS. Initially, the multi-scale decomposition technique effectively captures the multi-scale features of noisy speech signals. Subsequently, optimizing the time-frequency characteristics and dynamic signal adjustments effectively removes noise while improving the signal's time-frequency resolution. Finally, the method is further enhanced through the adaptive error minimization algorithm, leading to a more pronounced noise reduction effect. Experimental results demonstrate that the proposed method outperforms the single dynamic multi-scale technique in terms of improving signal-to-noise ratio (SNR).

关键词： Noise Heuristic algorithms signal processing algorithms Noise reduction Minimization Speech enhancement Time-frequency analysis Optimization signal processing Filtering algorithms Adaptive error multi-scale analysis optimal estimation speech noise reduction time-frequency characteristic optimization

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Advanced Near-Field Radar Imaging Approaches in Security: An overview on signal processing challenges, opportunities, and future directions

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IEEE signal processing MAGAZINE 2025年第1期42卷 126-146页

作者： Molaei, Amir Masoud Hu, Shaoqing Fusco, Vincent Fromenteze, Thomas Kumar, Rupesh Abbasi, Muhammad Ali Babar Yurduseven, Okan Queens Univ Belfast Ctr Wireless Innovat Belfast BT3 9DT North Ireland Brunel Univ London Coll Engn Design & Phys Sci Uxbridge UB8 3PH England Hangzhou Dianzi Univ Sch Elect & Informat Hangzhou 310005 Peoples R China Univ Limoges Xlim Res Inst F-87000 Limoges France Duke Univ Ctr Metamat & Integrated Plasmon Durham NC 27705 USA SRM Univ Andhra Pradesh Dept Elect & Commun Engn Amaravati 522502 Andhra Prades India

Near-field (NF) microwave and millimeter-wave (mm-wave) imaging, extending into the terahertz (THz) frequency range, has seen remarkable advancements across diverse applications, particularly security screening. These technologies benefit from the unique properties of microwave, mm-wave, and THz (MMT) spectra, such as penetration, nonionizing radiation, material sensitivity and the capability to operate in all weather conditions. This article provides an overview of the evolution and current state of NF radar imaging, emphasizing the critical role of signal processing in overcoming challenges related to hardware complexity, long acquisition time, and image reconstruction quality. Advanced signal processing techniques-including Fourier-based algorithms, sparse imaging, low-rank matrix recovery, and deep learning-are highlighted for their contributions to enhancing image resolution and processing efficiency. The article also discusses recent innovations in antenna technologies, aperture configurations, and scanning methods that have significantly improved NF radar imaging capabilities. Future research directions are suggested to further advance the field, highlighting the importance of continued exploration and innovation in NF MMT imaging.

关键词： Technological innovation Microwave technology Imaging signal processing algorithms Terahertz radiation Radar imaging signal processing Noise measurement Security Image reconstruction

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A Pre-processing signal Modeling Approach for Multichannel ATI Digitizers

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Mei, Sitao Zhao, Yijiu Shao, E. Wang, Yusen Zhang, Yi Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Asynchronous time-interleaved (ATI) analog-to-digital converter (ADC) architecture utilizes pre-processing, low-pass filtering, and ADCs to simultaneously enhance the sampling rate and bandwidth of the acquisition system. We propose a novel approach to generate pre-processing signal in multichannel ATI architecture. The generated pre-processing signal is no longer limited to pulse wave or square wave with a fixed duty. We derive the condition for reconstructing input signal as the invertibility of the pre-processing matrix. Additionally, we analyzed the error sources in the multichannel ATI. Based on the spectral shift characteristics of the ATI, we propose an error coefficient estimation method. Using the estimated error coefficient, a digital filter can be designed to calibrate the system. A four-channel hardware prototype was developed to evaluate the performance of the proposed method, satisfactory results were obtained and the feasibility of the proposed approach was demonstrated.

关键词： Low-pass filters Calibration Mixers Estimation Frequency response Filtering algorithms Time-frequency analysis signal processing algorithms Hardware Fourier series Analog-to-digital converters (ADCs) asynchronous time-interleaved (ATI) frequency-interleaved (FI) wideband

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Robust 3-D Multi-Source Localization With a Movable Antenna Array via Sparse signal processing

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 3664-3682页

作者： Mansourian, Amir Fadakar, Alireza Akhavan, Saeed Maham, Behrouz Univ Tehran Sch Elect & Comp Engn Tehran *** Iran Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA Nazarbayev Univ Dept Elect & Comp Engn Astana 010000 Kazakhstan

Accurately localizing multiple sources is a critical task with various applications in wireless communications, such as emergency services, including natural post-disaster search and rescue operations. However, scenarios where the receiver is moving have not been sufficiently addressed in recent studies. This paper tackles the angle of arrival (AOA) 3D-localization problem for multiple sparse signal sources with a moving receiver, which has a limited number of antennas that may be outnumbered by the sources. First, an energy detector algorithm is proposed to leverage signal sparsity for eliminating noisy samples. Subsequently, an iterative algorithm is developed to refine and estimate the AOAs accurately, initialized with previously estimated source locations and coarse elevation and azimuth AOAs obtained via the two-dimensional multiple signal classification (2D-MUSIC) method. To this end, we introduce a sparse recovery algorithm to exploit signal sparsity, followed by a phase smoothing algorithm to refine the estimates. The K-SVD algorithm is then applied to the smoothed output to accurately determine the elevation and azimuth AOAs of the sources. For localization, a new multi-source 3D-localization algorithm is proposed to estimate source positions using the refined AOA estimates over a sequence of time windows. Extensive simulations are carried out to demonstrate the effectiveness of the proposed framework.

关键词： Location awareness Receivers Three-dimensional displays Classification algorithms Vectors signal processing algorithms Azimuth Antenna arrays Accuracy Synchronization Angle of arrival estimation detection K-SVD localization sparse recovery sparsity

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Distributed algorithms for Array signal processing

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IEEE TRANSACTIONS ON signal processing 2021年 69卷 4607-4622页

作者： Chen, Po-Chih Vaidyanathan, P. P. CALTECH Dept Elect Engn Pasadena CA 91125 USA

Distributed or decentralized estimation of covariance, and distributed principal component analysis have been introduced and studied in the signal processing community in recent years, and applications in array processing have been indicated in some detail. Inspired by these, this paper provides a detailed development of several distributed algorithms for array processing. New distributed algorithms are proposed for DOA estimation methods like root-MUSIC, total least squares-ESPRIT, and FOCUSS. Other contributions include distributed design of the Capon beamformer from data, and distributed implementation of the spatial smoothing method for coherent sources. A distributed implementation of a recently proposed beamspace method called the convolutional beamspace (CBS) is also proposed. The proposed algorithms are fully distributed - an average consensus (AC) is used to avoid the need for a fusion center. The algorithms are based on a recently reported finite-time version of AC which converges to the exact solution in a finite number of iterations. Numerical examples are given throughout the paper to show the effectiveness of the proposed algorithms.

关键词： Estimation Direction-of-arrival estimation Distributed algorithms Array signal processing signal processing algorithms Convergence Smoothing methods Distributed or decentralized DOA estimation Capon beamforming convolutional beamspace root-MUSIC ESPRIT FOCUSS spatial smoothing

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Theory and algorithms for Pulse signal processing

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2020年第8期67卷 2707-2718页

作者： Nallathambi, Gabriel Principe, Jose C. Vital Connect Inc San Jose CA 95110 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

The integrate and fire converter (IFC) transforms an analog signal into a train of biphasic pulses. The pulse train has information encoded in the timing and polarity of pulses. While it has been shown that any finite bandwidth analog signal can be reconstructed from these pulse trains with an error as small as desired, there is a need for fundamental signal processing techniques to operate directly on pulse trains without signal reconstruction. In this paper, the feasibility of performing online the operations of addition, multiplication, and convolution of analog signals using their pulses train representations is presented. The theoretical framework to perform signal processing with IFC pulse trains imposing minimal restrictions is derived, and algorithms for online implementation of the operators is developed. The performance of the proposed algorithms is studied by quantifying the variations in instantaneous occurrence of pulses. Comparisons are performed with digital processing of reconstructed pulse trains. Moreover, an application of noise subtraction and representation of relevant features of interest in electrocardiogram signal is demonstrated with a sparse data rate of less than 20 IFC pulses per second, and an absolute error in heart rate of 0.16 +/- 0.18 bpm.

关键词： signal processing algorithms Convolution Timing Bandwidth Sensors Internet of Things Analog to pulse converter biphasic pulse trains convolution pulse signal processing semantic information

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signal processing and Machine Learning with Differential Privacy [algorithms and challenges for continuous data]

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IEEE signal processing MAGAZINE 2013年第5期30卷 86-94页

作者： Sarwate, Anand D. Chaudhuri, Kamalika Toyota Technol Inst Chicago IL 60637 USA Univ Calif San Diego Dept Comp Sci & Engn San Diego CA 92103 USA

Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing.

关键词： Privacy Data privacy signal processing algorithms Approximation methods Noise measurement Computer security

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Learning algorithms and signal processing for Brain-Inspired Computing

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IEEE signal processing MAGAZINE 2019年第6期36卷 12-15页

作者： Simeone, Osvaldo Rajendran, Bipin Gruning, Andre Eleftheriou, Evangelos S. Davies, Mike Deneve, Sophie Huang, Guang-Bin Kings Coll London Dept Engn Informat Engn Res London England New Jersey Inst Technol Newark NJ 07102 USA New Jersey Inst Technol Elect & Comp Engn Newark NJ 07102 USA IBM TJ Watson Res Ctr New York NY USA Univ Surrey Dept Comp Sci Computat Intelligence Surrey England Univ Bern Inst Physiol Bern Switzerland IBM Res Zurich Zurich Switzerland Intels Neuromorph Comp Lab Zurich Switzerland Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

The articles in this special section focuses on machine learning (ML) and signal processing algorithms for bio-inspired computing. The articles bring together key researchers in this area to provide readers of IEEE signal processing Magazine with up-to-date and survey-style articles on algorithmic, hardware, and neuroscience perspectives on the state-of- the-art aspects of this emerging field.

关键词： Special issues and sections Bio-inspired computing signal processing algorithms Neuromorphic engineering Neuroscience Machine learning

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FAST algorithms FOR ESTIMATION AND signal-processing - AN INVERSE SCATTERING FRAMEWORK

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND signal processing 1989年第6期37卷 957-959页

作者： YAGLE, AE Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

The inverse scattering problem for a lossless transmission line has been used as a framework for deriving the Levinson and Schur fast algorithms for Toeplitz matrices. Here, the same framework is used to derive and attach novel simple scattering interpretations to the lattice algorithm and the split algorithms. This demonstrates that the scattering and interpretation is more fundamental to fast signal-processing algorithms than previously noted.

关键词： signal processing algorithms inverse scattering Frameworks INVERSE SCATTERING PROBLEM algorithms Fasting signal processing Toeplitz matrices broadcasting

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Editorial on the Special Section on algorithms, Circuits, and Systems for signal processing at the Edge

IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS

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IEEE OPEN JOURNAL OF CIRCUITS AND SYSTEMS 2021年 2卷 766-768页

作者： Sousa, Leonel Sheikh, Farhana Bermak, Amine Univ Lisbon Inst Super Tecn INESC ID Lisbon Portugal Intel Corp Santa Clara CA 95054 USA Hamad Bin Khalifa Univ Coll Sci & Engn Ar Rayyan Qatar

Technological trends alongside with the unprecedented growth of the data generated by devices sparsely distributed, most of them mobile devices, cannot be supported by traditional approaches and processing systems. The requirement for computations at the edge are very stringent in terms of security, bandwidth, computational speed, latency and power consumption. These computations typically require signal processing algorithms running on resource-constrained mobile devices, while to provide the core services and the required computational power, cloud servers are applied. Leveraging services of cloud by mobile devices result in high latency and communication network and mobility-related issues, which can be mitigated by cleverly moving some of these computation to the edge. This special Section is exactly focused on addressing challenges raised by moving computation to the edge in a holistic way, by examining not only challenges related to algorithmic consideration and computational requirements but also circuits and systems and hardware platforms suitable for such implementation. The special Section also considers the trade-offs related to power and energy consumption, bandwidth versus computational complexity and security.

关键词： Special issues and sections Edge computing signal processing algorithms Energy consumption Power demand Mobile handsets Distributed databases

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