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检索条件"主题词=linear predictive coding"

共 2464 条记录，以下是21-30 订阅

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LOCALIZATION OF ACOUSTIC SOURCE ON SOLIDS: A linear predictive coding BASED ALGORITHM FOR LOCATION TEMPLATE MATCHING

LOCALIZATION OF ACOUSTIC SOURCE ON SOLIDS: A LINEAR PREDICTI...

引用

2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Yap, XueXin Khong, Andy W. H. Gan, Woon-Seng Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781424442966

Location template matching (LTM) is a source localization technique in solids that is robust to dispersion and multipath. This is possible since LTM compares the input with a database of signals made at known locations. With this in place, it is possible to employ LTM in situations where the surface of interest takes an irregular shape. However, one of the existing LTM approaches uses cross-correlation to compare the input and the database. It should be noted that if any two of the known locations stored in the database are too close, the cross-correlation method may have difficulties differentiating between signals generated from the neighboring points. To address this, we propose an algorithm which employs the linear predictive coding (LPC) that takes into account the dominant frequencies of a received signal. Using this approach, we show that the proposed algorithm is able to improve LTM's source localization accuracy under a real environment in the context of source localization for a touch interface.

关键词： user interfaces correlation linear predictive coding location template matching

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An Interactive Speech Therapy Session using linear predictive coding in Matlab and Arduino

An Interactive Speech Therapy Session using Linear Predictiv...

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International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

作者： Vijayalakshmi, R. Priya, S. SA Engn Coll Dept EEE Chennai Tamil Nadu India

ISBN: (纸本)9781467395458

Speech Therapy has become an efficient tool to bring back proper speech for patients suffering from various speech disorders. Patients are more benefitted when the speech therapy session is interactive where there is a visible change in the environment. Hence, the proposed system aims at manipulating devices when the user input is correct and also indicates if the user input is incorrect. Speech recognition has been done using the concept if linear predictive coding and Arduino Uno board is used for hardware interface.

关键词： Speech therapy Arduino linear predictive coding Speech processing ATmega328 microcontroller

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The application of linear predictive coding for RoIP data transmission in the HFC network 6

The application of Linear Predictive Coding for RoIP data tr...

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6th IEEE International Conference on Network Infrastructure and Digital Content (IEEE IC-NIDC)

作者： Cui, Chengyu Park, Sungkwon Jung, Joonyoung Lee, Jewon Hanyang Univ Dept Elect & Comp Engn Seoul South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781538660676

Recently, HFC (hybrid fiber cable) has been widely used due to the demand for economical and efficient digital services. HFC can be used for an integrated digital service broadband network access technology, through the photoelectric conversion process to achieve data transmission between different devices and equipment. Therefore, RoIP (Radio Over IP) technology for two-way communication is revealed without changing the original system hardware structure. RoIP is a technology that converts radio frequency signal generated at a transmitting end into digital signal and transmits it using an optical IP network. However, during the signal conversion process, a large amount of data generated needs to be compressed to achieve efficient data transmission. In this paper, we reduce the amount of data by combining linear predictive coding techniques, which is widely used in speech signal processing, with nonlinear quantization coding. By using this method, RF signals can be transmitted efficiently. We measured the compression ratio and the magnitude of the error vector representing the degree of signal damage during the compression process and compared it with previous experimental results to verify the availability of these schemes.

关键词： HFC RoIP IP network linear predictive coding Nonlinear quantization coding

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An application of linear predictive coding and computational geometry to iris recognition

An application of linear predictive coding and computational...

引用

Working Conference on Computer Vision

作者： Alipour, Masoud Farhadi, Ali Razavi, Nima Inst Studies Theoret Phys & Math Tehran Iran

The aim of this work is to present a method in computer vision for person identification via iris recognition. The method makes essential use of computational geometry and LPC. (C) 2007 Wiley Periodicals, Inc.

关键词： computer vision texture classification biometrics linear predictive coding computational geometry

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A Comparative Analysis on Cepstrum, linear predictive coding and Particle Filtering Based Formant Estimation Methods

A Comparative Analysis on Cepstrum, Linear Predictive Coding...

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22nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Resat, Mustafa Anil Gokcimen, Halil Ibrahim Arioz, Umut Gazi Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkey Gazi Univ Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781479948741

Formants are able to define basic properties of speech efficiently by using very limited parameter sets;thus they have found important usage area at many applications of speech processing like coding, recognition, synthesis and enhancement. Estimation of formants is harder than simply tracking the peaks of the spectrum;as the output of the vocal tract's spectral peaks are dependent on the shape of vocal tract, excitation and periodicity in a complex way. Because of this reason, a lot of past work was done on formant estimation and their positive and negative properties have been recognized. In this article we analyzed some of the popular formant estimation method's performances and compared them. Among these three compared methods, it's seen that the particle filtering based formant estimation method gives the most successful performance. Furthermore, it's recognized that linear predictive coding method has estimation difficulties with signals with low sampling frequencies and cepstrum method causes excess formants at peak picking.

关键词： cepstrum linear predictive coding particle filtering formant estimation

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Normal and Murmur Heart Sound Classification Using linear predictive coding and k-Nearest Neighbor Methods 3

Normal and Murmur Heart Sound Classification Using Linear Pr...

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3rd International Conference on Informatics and Computational Sciences (ICICoS)

作者： Sofwan, Aghus Santoso, Imam Pradipta, Himawan Arfan, M. Zahra, Ajub Ajulian M. Diponegoro Univ Dept Elect Engn Semarang Indonesia

ISBN: (纸本)9781728146102

Heart rate sounds have a special pattern that is in accordance with a person's heart condition. An abnormal heart will cause a distinctive sound called a murmur. Murmurs caused by various things that indicate a person's condition. Through a Phonocardiogram (PCG), it can be seen a person's heart rate signal wave. Normal heartbeat and murmurs have a distinctive pattern, so that through this pattern it can be detected a person's heart defects. This study will make a classification program that will sense normal heart sounds and murmurs. This program uses feature extraction methods using LPC (linear predictive coding) and classification using k-NN (k-Nearest Neighbor) to identify these 2 heart conditions. The data that will be used as a database consists of samples of normal heart rate sounds and murmurs, and also data obtained from the heart rate detection device in the .wav, mono format. The system for detecting heart abnormalities consists of three main parts, namely: recording heart rate sounds, feature extraction using LPC with order 10, and feature lines using k-NN with 3 types of distances and variations of k. From the results of testing with these types of distance, the obtained average accuracy value of Chebyshev, City Block, and Euclidean are 96.67, 91.67, and 93.33 percent, respectively. In addition, the value of k equal 3 is the most optimal value of k with an average level of 96.67 percent.

关键词： heart abnormalities linear predictive coding k-nearest neighbour

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Automatic Characterization and Detection of Behavioral Patterns Using linear predictive coding of Accelerometer Sensor Data

Automatic Characterization and Detection of Behavioral Patte...

引用

32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Min, Cheol-Hong Tewfik, Ahmed H. Univ Minnesota Twin Cities Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781424441242

In this study, we target to automatically detect behavioral patterns of patients with autism. Many stereotypical behavioral patterns may hinder their learning ability as a child and patterns such as self-injurious behaviors (SIB) can lead to critical damages or wounds as they tend to repeatedly harm one single location. Our custom designed accelerometer based wearable sensor can be placed at various locations of the body to detect stereotypical self-stimulatory behaviors (stereotypy) and self-injurious behaviors of patients with Autism Spectrum Disorder (ASD). A microphone was used to record sounds so that we may understand the surrounding environment and video provided ground truth for analysis. The analysis was done on four children diagnosed with ASD who showed repeated self-stimulatory behaviors that involve part of the body such as flapping arms, body rocking and self-injurious behaviors such as punching their face, or hitting their legs. The goal of this study is to devise novel algorithms to detect these events and open possibility for design of intervention methods. In this paper, we have shown time domain pattern matching with linear predictive coding (LPC) of data to design detection and classification of these ASD behavioral events. We observe clusters of pole locations from LPC roots to select candidates and apply pattern matching for classification. We also show novel event detection using online dictionary update method. We show that our proposed method achieves recall rate of 95.5% for SIB, 93.5% for flapping, and 95.5% for rocking which is an increase of approximately 5% compared to flapping events detected by using wrist worn sensors in our previous study.

关键词： ASD accelerometer accelerometers autism spectrum disorder behavioral event classification behavioral event detection behavioral patterns biomedical measurement children encoding linear predictive coding medical disorders medical signal processing microphone microphones online dictionary update method paediatrics pattern matching psychology signal classification stereotypical self-stimulatory behaviors time domain pattern matching wearable sensor wounds

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ON NOISE PROPAGATION IN CLOSED-LOOP linear predictive coding

ON NOISE PROPAGATION IN CLOSED-LOOP LINEAR PREDICTIVE CODING

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Krueger, Hauke Geiser, Bernd Vary, Peter Rhein Westfal TH Aachen Inst Commun Syst & Data Proc Aachen Germany

ISBN: (纸本)9781479928934

A new noise production and propagation model for open- and closed loop linear predictive coding (LPC) is proposed in this paper. The model allows to accurately predict the overall SNR even at lower bit rates where the conventional high rate theory fails. Moreover, a source of LPC encoder instabilities is pointed out which is due to the interaction between the quantizer and the (filtered) feedback of the quantization error. The new model is verified by measurements.

关键词： linear predictive coding quantization

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State-space approach to linear predictive coding of speech - A comparative assessment

State-space approach to linear predictive coding of speech -...

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8th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Irshad, Azeem Salman, Muhammad Natl Univ Sci & Technol Coll Elect & Mech Engn Islamabad Pakistan

ISBN: (纸本)9781467363228;9781467363204

Speech coders are fundamental component in telecommunication and multimedia infrastructure. Several systems like, mobile telephony, voice over internet protocol (VOIP), audio conferencing etc., rely on efficient speech coding. Speech coders strive to provide low-bit rate maintaining the same speech quality and intelligibility. linear predictive coding uses spectral properties of the speech to "optimize" the coder's performance for human ear. In this paper we perform a comparative assessment of speech coding performance of some state-space filters to give designers an insight into capabilities of these filters. The filters considered are Kalman filter, state-space recursive least-squares (SSRLS) and SSRLS with adaptive memory (SS-RLSWAM). The results of RLS and LMS are also quoted. The performance is judged in terms of perceptual evaluation of speech quality (PESQ) and prediction gain.

关键词： Speech coding Kalman Filter SSRLS SSRL-SWAM linear predictive coding

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Feature Extraction Methods based on linear predictive coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Feature Extraction Methods based on Linear Predictive Coding...

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2nd International Conference on Advances in Computing and Communications (ICACC)

作者： Sunny, Sonia Peter, David S. Jacob, K. Poulose Cochin Univ Sci & Technol Dept Comp Sci Kochi 682022 Kerala India Cochin Univ Sci & Technol Sch Engn Kochi 682022 Kerala India

ISBN: (纸本)9780769547237;9781467319119

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with linear predictive coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations.

关键词： Speech Recognition Feature Extraction linear predictive coding Wavelet Packet Decomposition Neural Networks

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