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

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2006年第5期16卷 230-233页

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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On predictive coding for Erasure Channels Using a Kalman Framework

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第11期57卷 4456-4466页

作者： Arildsen, Thomas Murthi, Manohar N. Andersen, Soren Vang Jensen, Soren Holdt Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark Univ Miami Coral Gables FL 33146 USA

We present a new design method for robust low-delay coding of autoregressive (AR) sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio (SNR) compared to the same coding framework optimized for no loss. The design method applies to stationary auto-regressive sources of any order. We demonstrate the method in a framework based on a generalized differential pulse code modulation (DPCM) encoder. The presented principles can be applied to more complicated coding systems that incorporate predictive coding as well.

关键词： Differential pulse code modulation (DPCM) erasure channels joint source-channel coding Kalman filtering linear predictive coding quantization

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Formant distortion after codecs for Arabic

Formant distortion after codecs for Arabic

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International Symposium on Communications Control and Signal Processing (ISCCSP)

作者： Amr Nabil Mohamed Hesham Faculty of Engineering Engineering Mathematics and Physics Department Cairo University Giza Egypt

In this work, we present results on the effect of wellknown mixed excitation linear prediction (MELP) and code-excited linear prediction (CELP) coders on the formants of Arabic sounds. The study shows, firstly, the spectral distortion of different Arabic phonemes due to mentioned coders. It was found that MELP1200 coder results in largest distortion. The coders effect on vowel formants are then investigated. The experiments show that the first formant of a vowel, on the average, suffers a frequency shift in the order of 50 Hz from its original value. Also, the amplitude distortion of the vowel formants due to Melp1200 coder was the highest, compared to the distortion of the other coders. Vowel formants have a shift which may exceed one critical band below or above its reference value.

关键词： Codecs Bit rate Speech analysis linear predictive coding Frequency Speech coding Power engineering and energy Mathematics Physics Process control

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On the use of perceptual Line Spectral Pairs Frequencies for speaker identification

On the use of perceptual Line Spectral Pairs Frequencies for...

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National Conference on Communications (NCC)

作者： Md. Sahidullah Goutam Saha Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9781424463831;9781424463855

Line Spectral Pairs Frequencies (LSFs) provide an alternative representation of the linear prediction coefficients. In this paper an investigation is carried out for extracting feature for speaker identification task which is based on perceptual analysis of speech signal and LSF. A modified version of the standard perceptual analysis is applied to obtain better performance. We have extracted the conventional LSF from the perceptually modified speech signal. State-of-the art Gaussian Mixture Model (GMM) based classifier is employed to design the closed set speaker identification system. The proposed method shows significant performance improvement over existing techniques in three different speech corpuses.

关键词： Speech analysis Feature extraction Signal analysis Speaker recognition Cepstral analysis Performance analysis predictive models Robustness Mel frequency cepstral coefficient linear predictive coding

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Enhancing sparsity in linear prediction of speech by iteratively reweighted 1-norm minimization

Enhancing sparsity in linear prediction of speech by iterati...

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

作者： Daniele Giacobello Mads Græsbøll Christensen Manohar N. Murthi Søren Holdt Holdt Jensen Marc Moonen Department of Electronic Systems University of Aalborg Denmark Department of Electrical and Computer Engineering University of Miami USA Department of Electrical Engineering (ESAT-SCD) Katholieke Universiteit Leuven Belgium

linear prediction of speech based on 1-norm minimization has already proved to be an interesting alternative to 2-norm minimization. In particular, choosing the 1-norm as a convex relaxation of the 0-norm, the corresponding linear prediction model offers a sparser residual better suited for coding applications. In this paper, we propose a new speech modeling technique based on reweighted 1-norm minimization. The purpose of the reweighted scheme is to overcome the mismatch between 0-norm minimization and 1-norm minimization while keeping the problem solvable with convex estimation tools. Experimental results prove the effectiveness of the reweighted 1-norm minimization, offering better coding properties compared to 1-norm minimization.

关键词： Speech enhancement Speech analysis Speech coding linear predictive coding Encoding predictive models Speech processing Shape Gaussian processes Bit rate

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A Discrete Wavelet Transform Based Approach to Hindi Speech Recognition

A Discrete Wavelet Transform Based Approach to Hindi Speech ...

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International Conference on Signal Acquisition and Processing, ICSAP

作者： Shivesh Ranjan Electronics and Communication Engineering Manipal Institute of Technology Manipal India

ISBN: (纸本)9781424457243;9780769539607

In this paper, we propose a new scheme for recognition of isolated words in Hindi Language speech, based on the Discrete Wavelet Transform. We first compute the Discrete Wavelet Transform coefficients of the speech signal. Then, linear predictive coding Coefficients of the Discrete Wavelet Transform coefficients are calculated. Our scheme then uses K Means Algorithm on the obtained linear predictive coding Coefficients to form a Vector Quantized codebook. Recognition of a spoken Hindi word is carried out by first calculating its Discrete Wavelet Transform Coefficients, followed by linear predictive coding Coefficient calculation of these Discrete Wavelet Transform Coefficients, and then deciding in favor of the Hindi word whose corresponding centroid (in the Vector Quantized codebook) gives a minimum squared Euclidean distance error with respect to the word under test.

关键词： Discrete wavelet transforms Speech recognition Multiresolution analysis linear predictive coding Testing Speech analysis Databases Natural languages Vectors Wavelet analysis

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Adapting entropy constrained coding of spectral envelope for fixed-rate coding in AMR speech codec

Adapting entropy constrained coding of spectral envelope for...

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Proceedings of the International Convention MIPRO

作者： T. Tadić D. Petrinović Research & Development Center Ericsson Nikola Tesla d.d. Zagreb Croatia Faculty of Electrical Engineering University of Zagreb Zagreb Croatia

The Adaptive Multirate (AMR) speech codec operates in 8 different fixed-rate modes. In every mode, it uses a specified number of bits to quantize and encode the current speech frame. It encodes the spectral envelope by means of Line Spectral Frequencies (LSF) parameters, by using the constrained resolution (CR) fixed-rate Split Matrix Quantization (SMQ) and Split Vector Quantization (SVQ) methods. However, by using the Gaussian Mixture Model (GMM) based transform coding technique, the quantization of the spectral envelope can be significantly improved in the spectral distortion (SD) sense. This technique involves adaptive decorrelation of the LSF vectors by applying an orthogonal linear transformation combined with an ordinary scalar quantization of the decorrelated vector's components. We apply uniform scalar quantization followed by entropy constrained (EC) coding as it appears to be generally more efficient than non-uniform scalar quantizers. This paper describes the techniques used to adapt the entropy coded variable bit-rate output bit strings to the AMR codec modes using fixed-rate output bit strings. In order to constrain the length of the code, we use variable quantization step size and vector truncation techniques. Their application aspects are thoroughly investigated and described in this paper.

关键词： Speech codecs Decorrelation Frequency Transform coding Nonlinear distortion Bit rate Entropy coding Vector quantization Speech coding linear predictive coding

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ESTIMATION OF FRAME INDEPENDENT AND ENHANCEMENT COMPONENTS FOR SPEECH COMMUNICATION OVER PACKET NETWORKS

ESTIMATION OF FRAME INDEPENDENT AND ENHANCEMENT COMPONENTS F...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Daniele Giacobello Manohar N. Murthi Mads Graesboll Christensen Soren Holdt Jensen Marc Moonen Dept. of Electronic Systems Aalborg Universitet Aalborg Denmark Dept. of Electrical and Computer Engineering University of Miami USA Dept. of Electrical Engineering Katholieke Universiteit Leuven Leuven Belgium

ISBN: (纸本)9781424442959

In this paper, we describe a new approach to cope with packet loss in speech coders. The idea is to split the information present in each speech packet into two components, one to independently decode the given speech frame and one to enhance it by exploiting inter-frame dependencies. The scheme is based on sparse linear prediction and a redefinition of the analysis-by-synthesis process. We present Mean Opinion Scores for the presented coder with different degrees of packet loss and show that it performs similarly to frame dependent coders for low packet loss probability and similarly to frame independent coders for high packet loss probability. We also present ideas on how to make the coder work synergistically with the channel loss estimate.

关键词： Speech coding Voice over IP (VoIP) linear predictive coding analysis-by-synthesis linear Prediction coding speech coding Packet loss ENCODER Packet switched networks Packet FRAMES linear prediction loss probability redefinition Speech Frame

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Low-Complexity PARCOR Coefficient Quantizer and Prediction Order Estimator for G.711.0 (Lossless Speech coding)

Low-Complexity PARCOR Coefficient Quantizer and Prediction O...

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Data Compression Conference (DCC)

作者： Yutaka Kamamoto Takehiro Moriya Noboru Harada NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation Atsugi Kanagawa Japan

ISBN: (纸本)9781424464258;9780769539942

This paper presents two low-complexity tools used for the new ITU-T recommendation G.711.0, which is the standard for lossless compression of G.711 (A-law/Mu-law logarithmic PCM) speech data. One is an algorithm for quantizing the PARCOR/reflection coefficients and the other is an estimation method for the optimal prediction order. Both tools are based on a criterion that minimizes the entropy of the prediction residual signals and can be implemented in a fixed-point low-complexity algorithm. G.711.0 with the developed practical tools will be widely used everywhere because it can losslessly reduce the data rate of G.711, the prevailing speech-coding technology.

关键词： Speech coding Phase change materials Entropy Signal processing Reflection Pulse modulation Codecs Quantization linear predictive coding Decoding

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Automatic filter design for synthesis of haptic textures from recorded acceleration data

Automatic filter design for synthesis of haptic textures fro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Joseph M. Romano Takashi Yoshioka Katherine J. Kuchenbecker Haptics Group GRASP Laboratory University of Pennsylvania USA Zanvyl Krieger Mind/Brain Institute Johns Hopkins University USA

Sliding a probe over a textured surface generates a rich collection of vibrations that one can easily use to create a mental model of the surface. Haptic virtual environments attempt to mimic these real interactions, but common haptic rendering techniques typically fail to reproduce the sensations that are encountered during texture exploration. Past approaches have focused on building a representation of textures using a priori ideas about surface properties. Instead, this paper describes a process of synthesizing probe-surface interactions from data recorded from real interactions. We explain how to apply the mathematical principles of linear predictive coding (LPC) to develop a discrete transfer function that represents the acceleration response under specific probe-surface interaction conditions. We then use this predictive transfer function to generate unique acceleration signals of arbitrary length. In order to move between transfer functions from different probe-surface interaction conditions, we develop a method for interpolating the variables involved in the texture synthesis process. Finally, we compare the results of this process with real recorded acceleration signals, and we show that the two correlate strongly in the frequency domain.

关键词： Filters Haptic interfaces Acceleration Surface texture Transfer functions Signal synthesis linear predictive coding Probes Cognitive science Virtual environment

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