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4th IFIP International Conference on Intelligent Information Processing

作者： Shrawankar, Urmila Mahajan, Anjali Govt Polytech Inst Dept Informat Technol Nagpur 440001 Maharashtra India Raisoni Coll Engg Dept Comp Sci & Engg Nagpur 440016 Maharashtra India

ISBN: (纸本)0387446397

Human computer interaction is concerned in the way Users (humans) interact with the computers. Some users can interact with the computer using the traditional methods of a keyboard and mouse as the main input devices and the monitor as the main output device. Due to one or another reason, some users are enable to interact with machines using a mouse and keyboard device, hence there is need for special devices. If we use computer for more time it is really difficult to sit on the chair, keeping hands continuously on the keyboard or mouse and keep watching the monitor. To relax our body and interact comfortably with Computer, we need some special device or method, so that computer Will understand and accept commands without keyboard or by clicking mouse. Speech Recognition System helps users who are unable to use traditional Input and Output (I/O) devices. Since four decades, man has been dreaming for an "intelligent machine" which can master the natural speech. In its simplest form, this machine should consist of two subsystems, namely Automatic Speech Recognition (ASR) and Speech Understanding (SU). The goal of ASR is to transcribe natural speech while SU is to understand the meaning of the transcription. Recognising and understanding a spoken sentence is obviously a knowledge-intensive process, which must take into account all variable information about the speech communication process, from acoustics to semantics and pragmatics.

关键词： Automatic Speech Recognition text-to-speech speech-to-text interactive voice response-systems linear prediction coding hidden Markov model

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Fault diagnosis of rolling bearing based on PSO and continuous Gaussian mixture HMM

Fault diagnosis of rolling bearing based on PSO and continuo...

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2015 2nd International Conference on Machinery,Materials Engineering,Chemical Engineering and Biotechnology(MMECEB 2015)

作者： Liao Guangchun Zhu Haiping Liu Kangjun Liao Jiawei Huazhong University of Science & Technology

ISBN: (纸本)9781510819092

As the hidden Markov model(HMM) has a strong ability of time sequence modeling,the continuous Gaussian mixture HMM is used to establish a model base of the rolling bearing *** adaptive particle swarm optimization(APSO) with extremum disturbed operator and dynamic change of inertia weights is introduced to the traditional training algorithm for solving the local extremum *** vibration signal is collected for extracting 12 order LPC coefficients as a feature vector through the dispose of adding *** the given feature vector,the HMM is built for bearing fault condition monitoring and fault ***,different fault conditions experiment are carried out on the motor bearing *** experiment result shows that the method can use a small amount of samples for training HMM,and it is more effective and has higher classification accuracy in fault diagnosis compared with the traditional training algorithm.

关键词： Particle Swarm Optimization Hidden Markov Model Fault diagnosis LPC Hidden Markov models Fault diagnosis PCSK7 gene linear prediction coding Particle swarm optimization rolling bearings Eigenvector parametric subharmonic oscillator extremum values

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Gesture recognition using HLAC features of PARCOR images

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2003年第4期E86D卷 719-726页

作者： Kurita, T Hayamizu, S Natl Inst Ind Sci & Technol Neurosci Res Inst Tsukuba Ibaraki 3058568 Japan Gifu Univ Gifu 5011193 Japan

This paper proposes a gesture recognition method which uses higher order local autocorrelation (HLAC) features extracted from PARCOR images. To extract dominant information from a sequence of images, we apply linear prediction coding technique to the sequence of pixel intensities and PARCOR images are constructed from the PARCOR coefficients of the sequences of the pixel values. From the PARCOR images, HLAC features are extracted and the sequences of the features are used as the input vectors of the Hidden Markov Model (HMM) based recognizer. Since HLAC features are inherently shift-invariant and computationally inexpensive, the proposed method becomes robust to changes in the person's position and makes real-time gesture recognition possible. Experimental results of gesture recognition are shown to evaluate the performance of the proposed method.

关键词： gesture recognition PARCOR images Hidden Markov Model higher order local autocorrelation linear prediction coding

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Two-Sided LPC-Based Speckle Noise Removal for Laser Speech Detection Systems

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第6期E104D卷 850-862页

作者： Wang, Yahui Zhang, Wenxi Kong, Xinxin Wang, Yongbiao Zhang, Hongxin Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Chinese Acad Sci Key Lab Computat Opt Imaging Technol Beijing Peoples R China

Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided linear prediction coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

关键词： speckle noise removal linear prediction coding speech enhancement laser speech detection

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Robust speech filtering in impulsive noise environments

Robust speech filtering in impulsive noise environments

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作者： Ledoux, Christelle Michelle Virginia Tech | University

This thesis presents a new robust filtering technique that suppresses impulsive noise in speech signals. The method makes use of Projection Statistics based on medians to detect segments of speech with impulses. The autoregressive model employed to smooth out the speech signal is identified by means of a robust nonlinear estimator known as the Schweppe-type Huber GM-estimator. Simulation results are presented that demonstrate the effectiveness of the filter. Another contribution of the work is the development of a robust version of the Kalman filter based on the Huber M-estimator. The performances of this filter are evaluated for a simple autoregressive process.

关键词： linear prediction coding robust statistics speech processing Thesis

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Application of Digital Processing of Heart Sounds To Heart Valve Modelling

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IFAC Proceedings Volumes 1982年第1期15卷 639-642页

作者： D. Nandagopal J. Mazumdar R.E. Bogner G. Karolyi Department of Electrical Engineering University of Adelaide G.P.О. Box 498 Adelaide South Australia Australia Department of Applied Mathematics University of Adelaide G.P.O. Box 498 Adelaide South Australia Australia

The application of spectral analysis of heart sound signals using linear prediction coding to determine the exact resonant frequencies corresponding to heart valves vibration is discussed. The resonant frequency of a cardiac valvular structure is an important parameter in determining the in-vivo constitutive properties and normal-pathogenic states of heart valves and ventricular myocardium. A peak augmentation technique for the spectral phonocardiagram is also demonstrated to obtain improved quality of resonance. The analysis is confined mainly to the second heart sound. As a result of such studies, important diagnostic information can be obtained, thereby enhancing the clinical significance of spectral phonocardiagraphy.

关键词： phonocardiagram signal processing linear prediction coding microprocessor modelling

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Modeling Uncertain Load and Wind Power in the Electric Energy Systems

Modeling Uncertain Load and Wind Power in the Electric Energ...

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IEEE Power and Energy Society General Meeting

作者： Noha Abdel-Karim Marija Ilic Engineering and Public Policy Department at Carnegie Mellon University Engineering and Public Policy Department and Electric and Computer Engineering Departments at Carnegie Mellon University

ISBN: (纸本)9781467327275

This paper is motivated by the recognition that sources of uncertainties in the electric power systems are multifold and that they may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, uncertain price of electricity and hard-to-predict power produced by the renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. It is, therefore, becoming very important to develop modeling methods for predicting uncertain load and wind power, in particular. In this paper we first transform historic time-stamped data into their Fourier Transform (FT) representation. The frequency domain data representation is used to decompose the wind and load power signals and to derive predictive models relevant for short-term and long-term predictions. The short-term results are interpreted next as a linear prediction coding Model (LPC) and its accuracy is analyzed. Next, the Discrete Markov Process (DMP) representation is applied to help assess probabilities of most likely short-, medium- and long-term states and the related multi-temporal risks. Throughout the paper we use publicly available data for the New York Control Area (NYCA).

关键词： Wind Power prediction Load Power prediction Short-term Decision Trees for Generation Scheduling Long-Term Decision Trees for Planning Discrete Markov Modeling of Wind Power Wind energy power systems ENERGY SYSTEMS Wind Plant uncertain linear prediction coding Uncertainty renewable resource electrical energy Energy sources LOAD available data paper Sun Electricity

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Relation of Resonant Frequency and Appropriate linear Predictive Order for Evaluating Concrete Strength by Quality Factor

Relation of Resonant Frequency and Appropriate Linear Predic...

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IEEE International Ultrasonics Symposium

作者： Ryo Toh Yahiro Mori Seiichi Motooka Department of Electrical Electronics and Computer Engineering Chiba Institute of Technology Department of Architecture and Civil Engineering Chiba Institute of Technology

ISBN: (纸本)9781467345613

Concrete is a popular construction material used broadly in many kinds of structures. The compressive strength is the most important performance of the concrete responsible to the safety of such structures. Although method of sampling a specimen is commonly employed, it makes damage to the structures. While Schmidt Hammer measures the hardness of the surface, but it relates to the compressive strength with poor accuracy owing to the influence of the conditions of the surface. Considering that the ultrasound propagating inside the concrete with higher strength will decrease less, the quality factor of the ultrasound signals should be higher, we have proposed a new method that evaluates the concrete strength non-destructively using the quality factor of resonant peak of multi-reflected ultrasound propagated inside the concrete. The quality factor is calculated by a linear predictive coefficient (LPC) processing on the ultrasonic signals, where the order of the LPC is an important parameter, by which the number of the poles of the LPC model is determined. Neither too many poles, which include too much details of the original signal with noise, nor too few poles, which lack of the proprieties at the resonant peaks, may express the propriety of ultrasonic propagation clearly. In this paper, as one of the approach of optimizing the parameters employed in measurement and signal processing, the relation between the resonant frequency and the appropriate order of LPC is discussed by numerical simulation and experiment. In the experiment, three types of concrete specimen with identical dimensions but different strengths (48.4 N/mm~2, 37.1 N/mm~2 and 27.9 N/mm~2 respectively) and four kinds of sound sources and receivers with different resonant frequency (28 kHz, 46 kHz, 68 kHz and 105 kHz, respectively) are employed. In the numerical simulation, a low-pass IIR filter is introduced to simulate the absorption of ultrasonic propagation in the concrete. Though the absorption is v

关键词： Quality factor resonant frequencies linear prediction coding PCSK7 gene concrete strength Infinite impulse response filters Concrete numerical simulation Ultrasonic propagation linear prediction Construction Materials Compressive strength Pole

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Audio modeling and loudness estimation with IJDSP mobile simulations

Audio modeling and loudness estimation with IJDSP mobile sim...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： G. Kalyanasundaram M. Banavar A. Spanias SenSIP Center Arizona State Univ. Tempe AZ USA

ISBN: (纸本)9781467369985

Audio signal modeling and simulation is important in several coding, noise removal, and recognition applications. This paper focuses on implementing models for loudness estimation and their use in estimating parameters on iOS mobile devices (iPhones and iPads). We briefly address estimating excitation patterns and loudness through auditory models. These loudness estimation and other algorithms were implemented in the award winning educational iOS app iJDSP for performing DSP simulations on mobile devices. The modules were introduced to graduate students in the general signal processing area, to evaluate their effectiveness as teaching tools. The evaluation process involved giving the students a pre-quiz, guiding them through hands-on activities on the iOS app, and finally, a post-quiz. Assessments results were positive with noticeable improvement of student understanding of topics such as spectrograms and linear predictive coding.

关键词： Loudness estimation auditory pattern iJDSP iOS mobile education loudness IO linear prediction coding Spectrogram Noise cancellation simulating Mobile devices Students Audio

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Automatic Speech Recognition bay System MFCC-LPC-SVM for energy optimization in intelligent building

Automatic Speech Recognition bay System MFCC-LPC-SVM for ene...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Henni Sid Ahmed Caelen Jean dept. Geni Electrica faculty science and technology University Abdelhamid Ibn Badis–Mostaganemm Route nationale Mostaganemmm Algeria LSI Carnot Institute (Software an Intelligent Systems Laboratory (LIG)CTL – Center for Software Technologies University Joseph Fourier – MultiCom Gières Grenoble France

ISBN: (纸本)9781728131573

the problem of smart buildings is their overconsumption of energy; in this article we propose a solution to this problem. This solution is to give optimum comfort to the people who occupy the smart buildings, but sometimes these people are not satisfied with this comfort, so we realized a system of automatic speech recognition, to give the possibility for these people to change the parameters of comfort, and to obtain the desired comfort. This solution allows to optimize the smart building energy, for example if a room is empty, then the person who has been in this room must say off when leaving this room, in this case the automatic recognition system of speech, instructs the system that managed the energy to turn off the lamps and lower the heater or air conditioner. This automatic speech recognition system using the MFCC method (Mel frequency cepstral coefficient) and a LPC method (linear prediction coding) for the representation of the speech signal, and SVM (support vector machine), for speech recognition. The recognition will be of global type, the speech signal will be parameterized using the MFCC coefficients and LPC coefficients to form the input vector of the recognition system, the SVM will be used for the learning phase and recognition phase. An experiment will be implemented using an input corpus, the vocabulary used in this corpus consists of 10 words in English language. The results obtained will be analyzed and compared with other similar works that use other techniques of automatic speech recognition.

关键词： MFCC LPC Speech processing SVM Classification Energy Intelligent building intelligent buildings Speech recognition Mel frequency cepstral coefficient linear prediction coding PCSK7 gene Support Vector Network Speech processing Energy sources Voice Signal COMFORT Taxonomy recognition system Persons

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