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19th International Audio Mostly Conference (AM)

作者： Silva, Nishal Turchet, Luca Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9798400709685

This paper investigates the real-time detection of predefined monophonic patterns from the MIDI output of a digital musical instrument. This enables the development of instruments and systems for live music, which can recognize when a musician plays a certain phrase and repurpose such information to trigger external peripherals connected to the instrument. Specifically, we compare the recognition performance of Dynamic time Warping and Recurrent Neural Network-based approaches. We employ different representation formats of musical data to optimize the efficiency of each computational method. To evaluate the algorithms, a novel dataset is introduced which includes recordings from 20 keyboard players and 20 guitar players. The evaluation focuses on the algorithms' ability to recognize patterns amid variations that impede a straightforward one-to-one comparison. The results reveal that both methods perform well in detecting up to 3 distinct patterns. However, as the number of different patterns increases up to 10, dynamic time warping exhibits a negative correlation with the recognition performance, while the recurrent neural network maintains high detection accuracy of approximately 98%. Taken together, our findings demonstrate the potential of machine learning in handling complex musical patterns in real-time, paving the way for novel applications involving smart musical instruments.

关键词： Smart Musical Instruments real-time pattern recognition Internet of Musical Things

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Demo of a Smart Musical Instrument-based real time pattern Detection System 24

Demo of a Smart Musical Instrument-based Real Time Pattern D...

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19th International Audio Mostly Conference (AM)

作者： Silva, Nishal Turchet, Luca Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9798400709685

This manuscript focuses on a real-time pattern detection system using smart musical instruments, and its importance in Internet of Musical Things (IoMusT) applications, where smart musical instruments equipped with wireless connectivity and embedded computing devices can detect musical patterns and use them as controls for various peripheral devices. The demonstration showcases a pattern detection algorithm controlled by a digital musical instrument. The algorithm is capable of identifying pre-defined patterns during live performances and using them to trigger peripheral devices and other stage equipment. The demo features two smart musical instruments, a smart guitar, and a smart keyboard, each equipped with embedded computing devices running the real-time pattern detection algorithm.

关键词： Smart Musical Instruments real-time pattern recognition Internet of Musical Things

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A real-time EMG pattern recognition method for virtual myoelectric hand control

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NEUROCOMPUTING 2014年 136卷 345-355页

作者： Xing, Kexin Yang, Peipei Huang, Jian Wang, Yongji Zhu, Quanmin Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp Beijing 100190 Peoples R China Huazhong Univ Sci & Technol Sch Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China China Univ Petr Coll Chem Engn Dept Chem Ind Equipment & Control Engn Qingdao 266580 Peoples R China Univ W England Dept Engn Design & Math Bristol BS16 1QY Avon England

This study proposes a real-time electro-myogram (EMG) pattern recognition approach for the control of multifunction myoelectric hands. In techniques, time and frequency information is extracted by wavelet packet transform (WPT) and the node energy of the WPT coefficients is selected as the feature of the EMG signals. Then a novel feature selection method based on a depth recursive search algorithm is developed so that the high-dimensional features can be reduced by a supervised feature reduction algorithm. Consequently, the support vector machine (SVM) is adopted to give the recognition result. In the experiment, a real-time EMG pattern recognition system is developed to control a virtual hand with EMG signals from antebrachium. The experimental results show both the high accuracy and better real-time performance of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： EMG real-time pattern recognition Wavelet packet Non-parametric weighted feature extraction SVM

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A preliminary analysis of analysis window size and voting size with a time delay for a robust real-time sEMG pattern recognition 11

A preliminary analysis of analysis window size and voting si...

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11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Kim, MinKyu Lee, JaeMin Ko, Hyungyu Kim, Keehoon Korea Inst Sci & Technol Interact & Robot Res Ctr 39-1 Hawolgok Dong Seoul 136791 South Korea Univ Sci & Technol HCI & Robot Daejeon South Korea

ISBN: (纸本)9781479953332

Myo-electric signals have been widely used in human-machine interfaces because these biosignal directly reflect human intentions to robots. The major difficulty of applying these biosignal in a pattern recognition system in real time is that they are unstable and vary in time. This instability occurs outside of the steady state of the signal, at the beginning and the ending of the motions. For real-time application users, the errors at the beginning of motion can lower the credibility of a pattern recognition system. In this sense, precise classification is the most significant factor for the system;thus the classification accuracy has higher priority compared to other factors. Generally, a trade-off relationship between the time delay of control commands and the classification accuracy has been known for sEMG users. Since parameters for signal processing can alter the sensitivity(time delay and accuracy) of the system, this study investigates limitations of a pattern recognition system due to transientstate errors. In particular, the performance of the system is analysed with respect to the analysis window size and the voting size of classification. Through an off-line simulation, we propose useful guidelines for the analysis window size and voting size in myoelectric signals for real-time applications.

关键词： sEMG signal real-time pattern recognition optimal parameters and time delay

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Performance Study of the First 2-D Prototype of Vertically Integrated pattern recognition Associative Memory

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2020年第9期67卷 2111-2118页

作者： Deptuch, Gregory Hoff, James Jindariani, Sergo Joshi, Siddhartha Li, Dawei Liu, Tiehui Ogrenci-Memik, Seda Olsen, Jamieson Tran, Nhan Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60201 USA

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high-luminosity Large Hadron Collider (HL-LHC) running conditions. Associative memory (AM)-based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available, and the speed performance must be improved over previous systems while maintaining a comparable number of patterns. The vertically integrated pattern recognition AM (VIPRAM) project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2-D prototype (protoVIPRAM00) in which the AM building blocks were designed to be compatible with the 3-D integration. In this article, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3-D stacking. We also present performance boundary characterization of the chip under extreme conditions.

关键词： Three-dimensional displays Detectors Two dimensional displays Cams pattern recognition Associative memory Prototypes 3DIC associative memory (AM) IC testing real-time pattern recognition

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Projection-mapping-based object pointing using a high-frame-rate camera-projector system

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ROBOMECH JOURNAL 2021年第1期8卷 1-21页

作者： Kumar, Deepak Raut, Sushil Shimasaki, Kohei Senoo, Taku Ishii, Idaku Hiroshima Univ Grad Sch Engn Higashihiroshima Japan Hiroshima Univ Digital Monozukuri Manufacturing Educ Res Ctr Higashihiroshima Japan Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Japan

The novel approach to physical security based on visible light communication (VLC) using an informative object-pointing and simultaneous recognition by high-framerate (HFR) vision systems is presented in this study. In the proposed approach, a convolutional neural network (CNN) based object detection method is used to detect the environmental objects that assist a spatiotemporal-modulated-pattern (SMP) based imperceptible projection mapping for pointing the desired objects. The distantly located HFR vision systems that operate at hundreds of frames per second (fps) can recognize and localize the pointed objects in real-time. The prototype of an artificial intelligence-enabled camera-projector (AiCP) system is used as a transmitter that detects the multiple objects in real-time at 30 fps and simultaneously projects the detection results by means of the encoded-480-Hz-SMP masks on to the objects. The multiple 480-fps HFR vision systems as receivers can recognize the pointed objects by decoding pixel-brightness variations in HFR sequences without any camera calibration or complex recognition methods. Several experiments were conducted to demonstrate our proposed method's usefulness using miniature and real-world objects under various conditions.

关键词： Physical security Cyber-physical systems High-speed camera-projector Informative projection mapping real-time pattern recognition visible light communication

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A Novel Feature Reduction Method for real-time EMG pattern recognition System

A Novel Feature Reduction Method for Real-Time EMG Pattern R...

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25th Chinese Control and Decision Conference (CCDC)

作者： Yang, Peipei Xing, Kexin Huang, Jian Wang, Yongji Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Zhejiang Univ Technol Coll Informat Engn Zhejiang 310032 Peoples R China Huazhong Univ Sci & Technol Dept Control Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781467355322;9781467355339

This paper proposes a novel feature reduction approach for real-time electromyogram (EMG) pattern recognition. This study extracts time and frequency information by wavelet packet transform (WPT) coefficients and uses the node energy as the feature to overcome the translation-invariant property of WPT. Then the non-parametric discriminant analysis (NDA) is used for feature reduction. Because of some inherent properties of the packet node energy, the within-class scatter matrix is usually singular in this approach, which makes feature project unavailable. To solve this problem, a recursive algorithm is proposed to discard some feature components that lead to singularity and contain relatively less discriminant information. Finally, the support vector machine (SVM) is used as the classifier and gives the recognition result. The corresponding pattern of the action could be recognized in a millisecond (ms). The experimental results show that the proposed method has strong robustness and good real-time performance.

关键词： EMG real-time pattern recognition wavelet packet non-parametric weighted feature extraction SVM

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A Novel Feature Reduction Method for real-time EMG pattern recognition System

A Novel Feature Reduction Method for Real-Time EMG Pattern R...

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第25届中国控制与决策会议

作者： Peipei Yang Kexin Xing ian Huang Yongji Wang Institute of Automation Chinese Academy of Science College of Information Engineering Zhejiang University of Technology Department of Control Science and Technology Huazhong University of Science and Technology

关键词： EMG real-time pattern recognition wavelet packet non-parametric weighted feature extraction SVM

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Nonstationary topological learning with bridges and convex polytopes: the G-EXIN neural network

Nonstationary topological learning with bridges and convex p...

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International Joint Conference on Neural Networks (IJCNN)

作者： Randazzo, Vincenzo Cirrincione, Giansalvo Ciravegna, Gabriele Pasero, Eros Politecn Torino DET Turin Italy Univ Picardie Jules Verne LTI Amiens France Univ South Pacific Suva Fiji Politecn Torino DAUIN Turin Italy

ISBN: (纸本)9781509060146

Non-stationary topological representation can be addressed in two ways, according to the application: life-long modeling or by forgetting the past. Life-long learning requires neural networks equipped with a tool for judging if a neuron has to be created for tracking the input distribution. It is always implemented as an isotropic criterion (a hypersphere centered at the winner weight vector represents the domain of the neuron). Instead, the G-EXIN neural network, presented here, uses an anisotropic convex polytope, which, models the shape of the neuron neighborhood. This idea allows to consider the boundaries of the Voronoi sets of data and controls the extent of the extrapolation. It also employs a novel kind of edge, called bridge, which carries information on the extent of the distribution time change. Indeed, the analysis of bridges, mainly their density, yields a deeper insight to the kind of non-stationarity. Both artificial and real examples are given of the advantages of this approach with regard to the ESOINN neural network, which is the best existing approach to life-long modeling.

关键词： convex polytope Curvilinear Component Analysis ESOINN fault diagnosis GCCA non-stationary data real-time pattern recognition unsupervised neural network

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Maximum-likelihood approximate nearest neighbor method in real-time image recognition

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pattern recognition 2017年第0期61卷 459-469页

作者： Savchenko, A. V. Natl Res Univ Higher Sch Econ Lab Algorithms & Technol Network Anal 136 Rodionova St Nizhnii Novgorod 603093 Russia

An exhaustive search of all classes in pattern recognition methods cannot be implemented in real-time, if the database contains a large number of classes. In this paper we introduce a novel probabilistic approximate nearest-neighbor (NN) method. Despite the most of known fast approximate NN algorithms, our method is not heuristic. The joint probabilistic densities (likelihoods) of the distances to previously checked reference objects are estimated for each class. The next reference instance is selected from the class with the maximal likelihood. To deal with the quadratic memory requirement of this approach, we propose its modification, which processes the distances from all instances to a small set of pivots chosen with the farthest-first traversal. Experimental study in face recognition with the histograms of oriented gradients and the deep neural network-based image features shows that the proposed method is much faster than the known approximate NN algorithms for medium databases. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Approximate nearest neighbor method Large database Maximum likelihood real-time pattern recognition Image recognition Probabilistic neural network HOG (histograms of oriented gradients) Deep neural networks

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