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检索条件"主题词=Signal processing algorithms"

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Automated Dynamic Stuttered Speech Recognition System and Conversion System using Mel Filter And Enhanced Logistic Regression Model

Automated Dynamic Stuttered Speech Recognition System and Co...

引用

Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： K. Nirosha N. Venkatesh School of CS&AI SR University Warangal Telengana India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Confidence and communication are both negatively impacted by stuttering, a speech condition that affects more than 70 million people globally. To improve fluency and intelligibility, the Stuttered Speech Recognition (SSR) system uses cutting-edge technologies such as the Weighted Mel Frequency Cepstral Coefficient algorithm and Machine Learning (ML) models to detect and fix speech defects. It can transform text into speech for assured communication and includes noise reduction for clarity. This goes beyond the scope of conventional speech recognition and holds great promise for helping stuttering people lead more fulfilling lives and be more integrated into society at large. The characteristic features of stuttering speech are an abundance of initial repetitions and prolongations. As a result, the stuttering episodes can be categorized using sound analysis. Finding specific stuttering occurrences, such as prolongations and repeats, in stuttered speech using a feature extraction technique is described in this study. To determine how effectively it can identify stuttering prolongations and repetitions, we apply the famous Mel Frequency Cepstral Coefficient (MFCC) feature extraction. Stuttering is a disease of neurodevelopment characterized by a lack of fluency in conventional speech patterns. Prolonged or repeated sounds, syllables, sentences, or phrases are examples of disfluency. Stuttering detection is challenging and complex since it involves pathology, psychology, acoustics, and signal processing, all of which make it an intriguing interdisciplinary domain study challenge. A thorough analysis of stuttering and disfluency classification methods is presented based on acoustic data as well as statistical and machine learning and deep learning approaches. This research is helpful for scholars, academicians and medical experts to identify the limitations in traditional models and to design efficient solutions for stuttered speech classification.

关键词： Pathology Machine learning algorithms signal processing algorithms Psychology Speech recognition Speech enhancement signal processing Feature extraction Mel frequency cepstral coefficient Medical diagnostic imaging

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Lightweight Frequency Domain Hybrid Active Noise Control System for Uncorrelated Disturbance

Lightweight Frequency Domain Hybrid Active Noise Control Sys...

引用

IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Shosuke Namekawa Yosuke Sugiura Tetsuya Shimamura Saitama University Saitama Japan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

Hybrid Active Noise Control (HANC) is one of effective architectures to control undesired noise. HANC has two major problems: high computational complexity, and performance degradation when external noise uncorrelated with the primary noise enters the error microphone. In this paper, we propose a new HANC architecture to address the two problems. The proposed method utilizes frequency domain update algorithm to reduce the computational complexity. Additionally, the proposed method generates the error signal for FBANC by subtracting control signal by FFANC from the error signal. This architecture can extract the components uncorrelated with the output of FFANC part from the error signal. Computer simulations demonstrate that the proposed method can reduce calculation cost and can not deteriorate when external noise enter the error microphone in addition to being high performance than the conventional HANC.

关键词： Degradation Costs Frequency-domain analysis Noise signal processing algorithms Computer architecture Control systems Computational complexity Microphones Convergence

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Ray-Space Constrained Multichannel Nonnegative Matrix Factorization for Audio Source Separation

Ray-Space Constrained Multichannel Nonnegative Matrix Factor...

引用

European signal processing Conference (EUSIPCO)

作者： Antonio J. Muñoz-Montoro Marco Olivieri Mirco Pezzoli Julio Carabias-Orti Fabio Antonacci Augusto Sarti Departamento de Ingeniería de Telecomunicación Universidad de Jaén Linares Spain Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Non-negative matrix factorization (NMF) has been widely adopted for the blind separation of acoustic sources. In the context of VR and AR applications, when several microphones are available the adoption of sound field representations such as spherical harmonics or ray space is shown to be effective in the NMF context. In this work, we propose a dictionary-based NMF-based model considering ray-space-transformed signals. The novelty of this approach is to account for the explicit modelling of the frequency dependency of the sound propagation from the source positions to the sensors. Spatially-constrained approaches aim at exploiting spatial information to improve the separation performance; however, they may not take advantage of possible priors given by representations of the data. The proposed approach allows us to exploit the source location model of the Ray Space through a predefined frequency-dependent dictionary of Ray Space patterns. Results demonstrate the competitive performance of the proposed method with respect to state-of-the-art NMF-based algorithms using real recordings.

关键词： Location awareness Source separation Dictionaries signal processing algorithms Position measurement Robustness Sensors Recording Reverberation Microphones

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Securing AODV against Wormhole Attacks through ARP

Securing AODV against Wormhole Attacks through ARP

引用

International Conference on signal processing and Communication Systems, ICSPCS

作者： Shansong Huang Raad Raad Faisel Tubbal Nidhal Odeh Sining Liu Wajid Ali Khan School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

We propose an algorithm to detect wormhole attacks based on the Address Resolution Protocol (ARP) for mobile networks which use On-demand Distance Vector (AODV) routing protocol. This novel approach differs from all existing methods, requiring no extra hardware or software equipment, and offers a simple detection method by avoiding complex calculations. Wormhole attacks, being among the most harmful, can result in data theft, discarding, tampering, and can even facilitate other attacks. Current defense algorithms typically require additional hardware or software and involve complex calculations at each node, sometimes failing in certain scenarios. Our algorithm aims to overcome these limitations.

关键词： Software algorithms signal processing algorithms Packet loss signal processing Software Hardware Vectors Routing protocols Reliability AODV

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Symbolic Representation for Time Series

Symbolic Representation for Time Series

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European signal processing Conference (EUSIPCO)

作者： Sylvain W. Combettes Charles Truong Laurent Oudre Université Paris Saclay Université Paris Cité ENS Paris Saclay CNRS SSA INSERM Centre Borelli Gif-sur-Yvette France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This study proposes a novel symbolic representation method for time series data called ASTRIDE. Unlike conventional symbolization techniques, our approach exhibits adaptability in two critical phases: firstly, during the temporal segmentation process, where it dynamically detects change-points in the signals, and secondly, in the sample quantization step, where it leverages quantiles. Additionally, we develop a data-driven edit distance measure for assessing the similarity of our symbolic represen-tations. We demonstrate the performance of our representation compared to standard symbolizations on classification tasks. Our algorithm is evaluated on 86 univariate time series data sets with equal length sourced from the U CR Time Series Classification Archive. An open source GitHub repository is made available to reproduce the experiments in Python.

关键词： Quantization (signal) Heuristic algorithms Time series analysis signal processing algorithms Europe Complexity theory Classification algorithms Indexes Standards Software development management

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Multi-View Ensemble Clustering Algorithm Weighted by Kernel Density Estimation

Multi-View Ensemble Clustering Algorithm Weighted by Kernel ...

引用

International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Hong Hu Xuejun Li Jing Liao School of Computer Science and Technology Southwest University of Science and Technology

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Most of the existing ensemble clustering algorithms improve the performance by weighting the basic clusters to reduce the influence of low-quality basic clusters on the final clustering results. Low-quality base clustering can be understood as misclassifying sample points, which are presented as discrete points in the co-association matrix. So, we proposed a multi-view ensemble clustering algorithm based on the weighting of kernel density estimation, starting from the density distribution of discrete points in the co-association matrix. Firstly, the different sets of basic clusters are made into a co-association matrix using the evidence accumulation model. Secondly, we converted the co-association matrix into a sparse matrix, and then we calculated the density distribution weights of the nonzero elements in the matrix using kernel density estimation. Next we let these weights multiply with the sparse matrix. Thirdly, we reduce the weighted sparse matrix to the shape of the initial co-association matrix to obtain the density-weighted co-association matrix. Next, we use the K-means clustering algorithm on the weighted co-association matrix to obtain the final clustering results. Finally, the algorithm is subjected to comparison experiments and ablation experiments on five commonly used datasets. The experimental results show that the proposed ensemble clustering algorithm with kernel density estimation weighting performs better than other comparative algorithms.

关键词： Shape Clustering algorithms signal processing algorithms Estimation signal processing Sparse matrices Matrix converters Kernel Time complexity Informatics

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Adaptive Beamforming Algorithm Based on Reconstructed Covariance Matrix and Adaptive Projection Gradient Descent

Adaptive Beamforming Algorithm Based on Reconstructed Covari...

引用

6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Kai Zheng Yang Shi Qiuxia Wang Pengyuan Zhao The School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This paper proposes an adaptive beamforming algorithm that updates the covariance matrix based on a recursive method. In beamforming technology, the estimation of the covariance matrix is a crucial aspect of all positioning algorithms. However, traditional covariance matrix estimations rely on static short-frame data, leading to biases in positioning results. Due to the dynamic nature of speech signals, this method replaces old data with new data to obtain a real-time covariance matrix, enhancing the accuracy of positioning results. It introduces a method for updating the old covariance matrix with new observational data. Additionally, mismatches in the steering vector can result in poor resolution of beamforming. To address this, we propose an adaptive projection gradient algorithm for weight estimation, which adaptively adjusts the learning rate and iteratively calculates weights, solving the issue of inaccurate calculation of steering vector weights, improving the resolution of beamforming, and achieving high-resolution imaging of sound source signals. Simulation results show that the proposed method effectively enhances the localization and identification of sound sources, further improving the resolution of beamforming.

关键词： Location awareness Accuracy Array signal processing Heuristic algorithms signal processing algorithms Estimation Vectors Covariance matrices signal resolution Image reconstruction

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Application of Ground Penetrating Radar with Controlled Antenna Pattern for Detection of Low-Contrast Objects

Application of Ground Penetrating Radar with Controlled Ante...

引用

Microwave Conference (RMC), All-Russian

作者： Vladimir Ivanovich Sakhterov Aleksandr Anatolevich Averin Vladimir Viktorovich Varenkov Dmitri Sergeevich Gorkin Dmitri Andreevich Smirnov Scientific instrumentation sector Pushkov Institute of Terrestrial Magnetism Ionosphere and Radio Wave Propagation Moscow Russia

ISBN: (数字)9798331540944

ISBN: (纸本)9798331540951

Currently, ground penetrating radars are widely used, mainly classical shallow ones, based on stroboscopic oscilloscope technologies. In the specialized segment of geological exploration, deep pulse ground penetrating radars with ultra-wideband signals of increased amplitude are quite widely used. All the named devices have a fixed radiation pattern of antenna-feeder devices. A method for controlling the radiation pattern of the transmitter antenna is proposed. A method for controlling the radiation pattern of the receiving antenna is considered. The use of the proposed methods in combination allows obtaining several signals with different phase amplitude characteristics at one point for processing without mechanical movement of the device. The development of new methods for signal processing will increase the probability of detecting low-contrast objects.

关键词： Ground penetrating radar Geology Transmitting antennas signal processing algorithms Receiving antennas Oscilloscopes signal processing Radar antennas Antenna radiation patterns Ultra wideband technology

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Deep Learning Applications

Deep Learning Applications

引用

IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Jenq-Foung Yao Yu-Hsiang John Huang Cheng-Ying Yang Min-Shiang Hwang Information Systems and Computer Science Georgia College & State University Milledgeville GA USA Computer Science Univerwsity of Taipei Taipei R.O.C Computer Science & Information Engineering & Fintech and Blockchain Research Center Asia University Taichung R.O.C

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

This article presents an in-depth look at deep learning, a branch of machine learning that emphasizes training artificial neural networks with multiple layers to carry out intricate tasks with little human oversight. It explores the foundational concepts of artificial neural networks, highlighting their brain-inspired structure, layered architecture, and the processes by which they learn and extract features from data. The article delves into the training methods for optimizing these networks, including large datasets, backpropagation, and optimization algorithms. Additionally, it examines the wide-ranging applications of deep learning across various domains, such as computer vision, natural language processing, and complex gaming. Through this exploration, the article illustrates the power and versatility of deep learning, emphasizing its transformative impact on technology and its potential to drive innovation across numerous fields.

关键词： Deep learning Training Technological innovation signal processing algorithms Artificial neural networks signal processing Feature extraction Natural language processing Biological neural networks Optimization

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Joint Estimation of Channel, Range, and Doppler for FMCW Radar with Sparse Bayesian Learning

Joint Estimation of Channel, Range, and Doppler for FMCW Rad...

引用

IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Mehdi Ashury Fangqing Xiao José Rodríguez-Piñeiro Dirk Slock Peter Gerstoft Christoph F. Mecklenbräuker Dominik Lungenschmied Infineon Technologies Linz Austria Institute of Telecommunications TU Wien Vienna Austria EURECOM Sophia-Antipolis France College of Electronics and Information Engineering Tongji University Shanghai China NoiseLab UC San Diego CA

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

The adoption of Advanced Driver Assistance Systems (ADAS) and autonomous driving systems poses great challenges for vehicular communication and sensing architectures. The joint design of vehicular communication and sensing systems brings benefits both in performance, size, cost, and power consumption, and it enables cooperative perception, in which the local information from all vehicles is fused. We propose using joint estimation of channel, range, and Doppler frequency for Frequency Modulated Continuous Wave (FMCW) radar. Based on Sparse Bayesian Learning (SBL), this enables the use of prior knowledge (as local estimates from neighboring vehicles) in the data processing. This provides more reliable and accurate sensing than traditional radars, which only rely on the detection of LOS objects for a single vehicle. Besides, SBL increases the channel estimation accuracy, which constitutes a basis for the optimization of the transceivers/sensing nodes and reduces the probability of false alarm. Our represents an efficient framework for cooperative sensing in ADAS applications and contributes to the convergence of communication and sensing applications for connected vehicles.

关键词： Wireless communication Estimation Channel estimation signal processing algorithms Radar Frequency estimation Doppler radar Sensors Bayes methods Doppler effect

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