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The Harmonic Shift Algorithm for Efficient Multi-Pitch Detection

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 548-561页

作者： Grinewitschus, Lukas Jung, Peter Univ Duisburg Essen Dept Commun Technol D-47057 Duisburg Germany

Existing approaches for multi-pitch detection have still been highly complex. In this paper, the harmonic shift algorithm (HSA) for multi-pitch detection is illustrated. The HSA is a noncoherent blind detection scheme, exploiting the constant-Q transform (CQT). The only requirement for the successful application of the HSA is that all the sources are harmonic sources. Due to this simplicity, the HSA is considered viable for both single-pitch and multi-pitch detection, making its attractive for the application in other areas of signal processing. When illustrating the performance capabilities of the HSA, we demonstrate that it outperforms the state of the art regarding the computational complexity providing detection precision, accuracy and a recall factor comparable with existing schemes.

关键词： Harmonic analysis Transforms Hidden Markov models Convolutional neural networks signal processing algorithms Frequency estimation Computational complexity Audio signal processing blind detection constant-Q transform harmonic shift algorithm multi-pitch detection noncoherent detection

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Toward Generalization of sEMG-Based Pattern Recognition: A Novel Feature Extraction for Gesture Recognition

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Shen, Cheng Pei, Zhongcai Chen, Weihai Wang, Jianhua Zhang, Jianbin Chen, Zuobing Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310052 Zhejiang Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Zhejiang Univ Coll Med Affiliated Hosp 1 Dept Rehabil Med Hangzhou 310003 Zhejiang Peoples R China

Gesture recognition via surface electromyography (sEMG) has drawn significant attention in the field of human-computer interaction. An important factor limiting the performance of sEMG-based pattern recognition (PR) is the generalization ability which sEMG changes for the identical movements when conducted at various positions or by different persons. Thus, this study aims to explore the generalization of classifier to develop a stable classification model that does not require relearning, even if it is used by other people. We propose a new feature extraction method to diminish the influence of limb position on sEMG-based PR. Specifically, the sEMG features are extracted directly from time domain. This condition is accomplished by using Fourier transform properties, difference, and the sum of squares differences. The best offline cross-validation accuracy (CVA) results are 88.775% training data from the tenth subject and testing data from the fifth subject in the NinaPro dataset. The best online CVA is 99%, and the movement selection time is 47.036 +/- 1.028 ms. In comparison with the well-known sEMG feature, the CVA and the generalization of the proposed features improved substantially. These improvements aim to facilitate the practical implementation of myoelectric interfaces.

关键词： Feature extraction Time-domain analysis Electromyography Muscles Pattern recognition Speech recognition signal processing algorithms Generalizability gesture recognition pattern recognition (PR) signal processing surface electromyography (sEMG)

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Deep Optimization of Parametric IIR Filters for Audio Equalization

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 1136-1149页

作者： Pepe, Giovanni Gabrielli, Leonardo Squartini, Stefano Tripodi, Carlo Strozzi, Nicolo Univ Politecn Marche Dept Informat Engn Via Brecce Blanche I-60131 Ancona AN Italy ASK Ind SPA Viale Ramazni I-42124 Reggio Emilia Italy Univ Politecn Marche Dept Informat Engn Via Brecce Bianche I-60131 Ancona Italy

This paper describes a novel Deep Learning method for the design of IIR parametric filters for automatic multipoint audio equalization, that is the task of improving the sound quality of a listening environment at multiple listening points employing multiple loudspeakers. The filters are designed to approximate the inverse of the RIR and achieve almost flat magnitude response. A simple and effective neural architecture, named BiasNet, is proposed to determine the IIR equalizer parameters. This novel architecture is conceived for optimization and, as such, is able to produce optimal IIR equalizer parameters at its output, after training, with no input required. In absence of input, the presence of learnable non-zero bias terms ensures that the network works properly. An output scaling method is used to obtain accurate tuning of the IIR filters center frequency, quality factor and gain. All layers involved in the proposed method are shown to be differentiable, allowing backpropagation to optimize the network weights and achieve, after a number of training iterations, the optimal output according to a given RIR. The parameters are optimized with respect to a loss function based on a spectral distance between the measured and desired magnitude response, and a regularization term is used to keep the same microphone-loudspeaker energy balance after equalization. Two experimental scenarios are employed, a room and a car cabin, with several loudspeakers. The performance of the proposed method improves over the baseline techniques and achieves an almost flat band at a lower computational cost.

关键词： Equalizers Optimization Neural networks Speech processing signal processing algorithms MIMO communication Finite impulse response filters Deep learning optimization audio equalization machine learning IIR filtering parametric equalizer inverse filtering

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High Performance VLSI Architecture of FIR Filter for Seismic signal processing

High Performance VLSI Architecture of FIR Filter for Seismic...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： S. Karunakaran Electronics and communication Engineering Vardhaman college of Engineering Hyderabad India

As a primary requirement, FIR filters(Finite Impulse response)used in real-time seismic alert systems are required to be low complexity, fast, and re-configurable. The CSE technique is commonly used to abate hardware complexity in digital FIR filters by decreasing logic operators and logic depths. In this paper, we present a new LO and LD minimization matrix grouped CSE algorithm that outperforms existing CSE algorithms. Additionally, the suggested design With low complexity and reduced truncation error, we developed a new biased-based rounding method that includes half units, as well as to lessen critical-path delay, use the cut-set retiming technique. This paper presents two hardware-efficient FIR filter topologies (I and II) that employ cut-set retiming, HUB rounding, and the canonical signed digit based MCSE algorithm. Additionally, architecture II implements a reconfigurable FIR filter in a hardware- efficient manner. FPGA and ASIC platforms are used to implement the architectures on the hardware side. Comparing the hardware implementation of proposed architectures, we mooted decrease of LOs, CPD, effective latency, area-delay product (ADP), and power- delay product using cutting-edge CSE- based architectures by a factor of 2 more than 35%, 47 %, 36 %, 34 %, and 29 %,*** comparison to the *** reconfigurable architecture II has an approximately 46 %, 10 %, and 11 % less in PDP, ADP and CPD.

关键词： Finite impulse response filters signal processing algorithms Very large scale integration signal processing Hardware Real-time systems Complexity theory

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A Music Classification Approach Based on the Trajectory of Fifths

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IEEE ACCESS 2022年 10卷 73494-73502页

作者： Lukaszewicz, Tomasz Kania, Dariusz Silesian Tech Univ Fac Automat Control Elect & Comp Sci PL-44100 Gliwice Poland

In this paper we examine the applicability of the trajectory of fifths as a source of knowledge in automated music classification processes. The study shows that such trajectories provide valuable information concerning the harmonic structure of a given piece of music. The results of the conducted experiments indicate that even basic coefficients quantifying the trajectory of fifths (e.g. the length of the trajectory) can be utilized as feature variables in music classification algorithms. The performed experiments involved the computation of the trajectories of fifths for the music pieces from two genre groups - rock/pop and jazz, calculation of their basic coefficients, and using these coefficients as feature variables for various rock/pop - jazz classifiers based on popular machine learning algorithms, i.e. Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Linear Discriminant Analysis, Gaussian Naive Bayes, and C-SVM. The tests evaluating the performance of the established classifiers have shown that in all of the considered cases the mean balanced accuracies were greater than 0.900, reaching the maximum of 0.936 (std=0.019) for the C-SVM classifier and the minimum of 0.910 (std=0.017) for the classification using the Decision Tree algorithm. Given the basic nature of the utilized coefficients and the relative simplicity of the applied classification models, the achieved results seem very promising. They strongly encourage further research concerning the use of the trajectory of fifths in different music classification algorithms - especially evaluation of its potential in the multi-class or multi-label genre classification scenarios.

关键词： Trajectory Music Classification algorithms Harmonic analysis Aggregates signal processing algorithms Instruments Music classification computer music analysis music information retrieval music data mining

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IQ Symbols processing Schemes With LSTMs in OFDM System

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IEEE ACCESS 2022年 10卷 70737-70745页

作者： Li, Jun Xin, Tongliang He, Bo Li, Wenxin Qilu Univ Technol Sch Informat & Automat Shandong Acad Sci Jinan 250353 Peoples R China Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

IQ modulation enjoys great popularity in wireless communication. Its main advantage is the symmetric convenience of combining independent signal components into a single composite signals in the transmitter. And it splits the composite signal into its independent components in the receiver. However, due to the limitations of communication facilities and imperfect channel environments, the symbols will be distorted. In this paper, we propose several signal processing schemes based on deep learning (DL) neural networks (i.e., long short-term memory (LSTM)) to process signal in an end-to-end manner, known as DLA, EDLA and PDNet. These schemes combine with advanced DL architectures and data-driven models for complex-valued signal processing. Enlightened by IQ demodulation ideas, we adopt LSTMs to develop several schemes to implement different functions (i.e., signal detection and peak to average power ratio (PAPR) reduction). The simulation results show that the schemes have significant performance improvement in bit error rate (BER) while reducing the probability of high PAPR.

关键词： Modulation signal processing signal processing algorithms Frequency modulation Symbols Computer architecture Demodulation IQ modulation deep learning ensemble networks OFDM system

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A Rationale for Backprojection in Spotlight Synthetic Aperture Radar Image Formation

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2022年第5期37卷 44-+页

作者： Bauck, jerald L. Tempe 85283 AZ United States

This note on backprojection for spotlight synthetic aperture radar image formation is mainly pedagogic in purpose and is intended to be accessible. The presentation from first principles is elementary and detailed, beginning with the wave equation and melding wave notions with signal processing notions using a compact and consistent notation throughout. A reflection model is developed including a general expression for the receiver signal, which does not depend on a particular transmitted waveform. Then, the signal is specialized to monochromatic waves to show how waves and the Fourier transform fit together. In the end, the signal is once again generalized so that the theory works for any signal type. Backprojection is shown to reconstruct the wave field that was lost by sampling it at only one point, the receiving antenna. After specializing some details to the synthetic aperture radar geometry, the Projection Slice Theorem is introduced late, after an understanding of the underlying principles is obtained. Computational aspects are considered and it is seen that backprojection and direct Fourier inversion, also known as the polar format algorithm, are fundamentally the same, differing only in some implementation details, albeit significant ones, thus overturning the notion that backprojection is not a Fourier process. Those who might benefit from this article include people who have worked in this field and who seek a somewhat different point of view from the usual presentation, people in other fields who are unfamiliar with some of the engineering concepts involved, and signal processing engineers who appreciate a bit of wave theory.

关键词： Synthetic aperture radar Radar Mathematical models Computed tomography Spaceborne radar signal processing algorithms Geometry Reflection Mathematical models Image reconstruction Genetic expression Fourier transform Tutorials Autonomous aerial vehicles

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Deep Unrolled Recovery in Sparse Biological Imaging: Achieving fast, accurate results

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IEEE signal processing MAGAZINE 2022年第2期39卷 45-57页

作者： Ben Sahel, Yair Bryan, John P. Cleary, Brian Farhi, Samouil L. Eldar, Yonina C. Broad Inst MIT & Harvard Univ Cambridge MA 02142 USA Broad Inst MIT & Harvard Univ Opt Profiling Platform Cambridge MA 02142 USA Weizmann Inst Sci Dept Math & Comp Sci IL-7610001 Rehovot Israel MIT 77 Massachusetts Ave Cambridge MA 02139 USA Duke Univ Durham NC USA

Deep algorithm unrolling has emerged as a powerful, model-based approach to developing deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization. This framework is well suited to applications in biological imaging, where physics-based models exist to describe the measurement process and the information to be recovered is often highly structured. Here we review the method of deep unrolling and show how it improves source localization in several biological imaging settings.

关键词： Location awareness Learning systems Biological system modeling Pipelines Imaging signal processing algorithms Performance gain

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A Robust Passive Islanding Detection Technique With Zero-Non-Detection Zone for Inverter-Interfaced Distributed Generation

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IEEE ACCESS 2022年 10卷 96296-96306页

作者： Pangedaiah, B. Reddy, P. L. Santosh Kumar Obulesu, Y. P. Kota, Venkata Reddy Alghaythi, Mamdouh L. Lakireddy Bali Reddy Coll Engn Elect & Elect Engn Dept Mylavaram 533003 Andhra Pradesh India Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India JNTUK Elect & Elect Engn Dept Kakinada 533003 Andhra Pradesh India Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Al Jouf Saudi Arabia

In this paper, a new and efficient passive islanding detection technique for a grid-connected hybrid distributed generating system has been proposed using Second Generation Wavelet Transform (SGWT) and Maximum Overlap Discrete Wavelet Transform (MODWT). In this technique, the ripple content of the voltage signal is measured at the Point of Common Coupling (PCC) and it is monitored through the decomposition of the signal by applying SGWT and MODWT technique up to suitable finer levels. This algorithm able to detects all kinds of islanding at nearly 0.3 sec, even with zero power mismatch. It is verified on a wide range of operational conditions and is also validated in various non-islanding conditions. Furthermore, the proposed technique is simple, there is no need for a classifier requirement, not depends on system parameters, zero non-detection zone (NDZ), and its operation is independent of the capacity and nature of distributed generation (DG) connected to the utility grid.

关键词： Islanding Discrete wavelet transforms signal processing algorithms Voltage measurement Renewable energy sources Classification algorithms Approximation algorithms Power grids Time-domain analysis Grid connected distributed generation maximum overlap discrete wavelet transform passive islanding detection second generation wavelet transform time domain analysis zero-non-detection zone

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Unsupervised and Supervised Learning Combined Power Load Curve Classification Based on Sequential Trajectory Feature Extraction Algorithm

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IEEE ACCESS 2022年 10卷 90312-90320页

作者： Zhang, Jing Zhang, Yikun Xu, Xiao Fu, Xiaofan He, Yu Guizhou Univ Sch Elect Engn Guiyang 550025 Guizhou Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China China Elect Power Res Inst Power Automat Dept Beijing 100192 Peoples R China

Advancements in smart grid technology and the extensive applications of electric power big data have made in-depth exploration of the behavioral characteristics of power consumers highly necessary for further development of the electricity market. This paper proposes an effective and interpretable load curve classification method that based on sequential trajectory feature learning and a random forest algorithm. Firstly, the unsupervised K-medoid clustering algorithm is used to obtain and filter precise category labels. Next, the fused lasso generalized eigenvector (FLAG) technique is used to search for interpretable sub-sequences from the labeled data in order to properly account for the sequential trajectory feature of load curves and increase the speed of the computation process. Following that, shapelet transformation is used to extract the sequential trajectory features from original data. Finally, in order to inherit the interpretability of shapelet, the random forest is trained on the sequential trajectory features. The simulated examples based on the real load curves of the specific city in China were investigated in order to assess the performance of the proposed load curve categorization approach. The results of the simulation demonstrate that the proposed approach has considerable advantages in terms of effectiveness, accuracy, and interpretability of load classification.

关键词： Classification algorithms Clustering algorithms Random forests Load modeling Feature extraction Data mining signal processing algorithms Unsupervised learning Supervised learning Sequential analysis Load curve classification K-mediods shapelet random forest sequential trajectory features unsupervised learning supervised learning

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