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Alveolar Consonant Recognition of Malay Children Using Neural Networks

11th International Congress of the IUPESM/World Congress on Medical Physics and Biomedical Engineering

作者： Ting, H. N. Lee, J. X. Univ Malaya Fac Engn Dept Biomed Engieering Jalan Pantai Baharu Kuala Lumpur 50603 Malaysia

ISBN: (纸本)9783642038815

This paper presents the speech recognition of Malay Alveolar consonants of Malay children using neural networks in a speaker-independent manner. The Alveolar consonants consist of /d/, /t/, /l/, /r/, /s/, /z/ and /n/. The Alveolar consonants are combined with six Malay vowels to form the consonant-vowel (CV) syllable sounds. 100 children are involved in the speech recording with a total of 4200 speech sounds. The speech sounds are recorded at a sampling rate of 20 KHz with 16-bit resolution. linear predictive coding (LPC) is used to extract the speech feature extraction. Multi-layer Perceptron (MLP), which is one of the most popular neural networks, is used to classify the Alveolar consonants. Experiments are conducted to determine the optimal signal length of the consonants, and hidden neuron number of MLP. A maximum recognition rate of 62.14% is obtained at signal lengths of 150ms and 160ms.

关键词： Alveolar linear predictive coding Malay Multi-layer Perceptron Speaker-independent

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A Method for Mass Estimation of Loose Parts in Nuclear Power Plant Based on Support Vector Machine

A Method for Mass Estimation of Loose Parts in Nuclear Power...

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9th International Symposium on Measurement Technology and Intelligent Instruments

作者： Zheng, Huawen Cao, Yanlong Yang, Jiangxin He, Yuanfeng Zhejiang Univ Inst Mfg Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)0878492739

A new method for mass estimation of loose parts in nuclear power plant (NPP) based on the support vector machine (SVM) was proposed. It includes analyses of the relationship between the impact signals' frequency spectrum and the mass of loose part, then formation of a vector consisting of linear predictive coding (LPC) parameters, which represent the shape of spectrum of impact signal. Using the vector as input data and the mass of loose part as the output data to train the SVM, the mass estimation can be done by the trained SVM model. Experimental results show that the method has higher accuracy and easier to achieve than the traditional methods. It provides a new way for mass estimation of loose part in NPP.

关键词： Loose Part Mass Estimation Support Vector Machine linear predictive coding

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Energy-Aware Compress and Forward systems for Wireless Monitoring of Time-Varying Fields

Energy-Aware Compress and Forward systems for Wireless Monit...

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21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Savazzi, S. Molteni, D. Spagnolini, U. Politecn Milan Dipartimento Elettron & Informaz Milan Italy

ISBN: (纸本)9781424480166

Dense monitoring of time-varying 2D field by Wireless Sensor Networks (WSN) needs to define new strategies to aggregate and encode data according to 2D sensor deployments. In this paper optimal design of communication protocol for WSN is conveniently cast for a set of linear sensor networks (sensor arrays) synchronously monitoring a correlated 2D time-varying field (e. g., for tracking time variations). Sensors aggregate data hop-by-hop by compressing the new samples gathered from the sensor board and forwarding towards the next sensor in the route. Compression is based on a linear predictive encoding to exploit the correlation properties of the field. Cross-layer design of source, channel coding and medium access control are jointly analyzed for optimal resource allocation and interference management to minimize energy consumption. Seismic monitoring application is analyzed to corroborate the design approach.

关键词： access protocols channel coding channel coding communication protocol dense monitoring energy-aware compress and forward systems linear predictive coding linear predictive encoding linear sensor networks medium access control seismic monitoring application sensor arrays sensor arrays source coding source coding time-varying 2D field time-varying fields wireless monitoring wireless sensor networks wireless sensor networks

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LOW-COMPLEXITY PARCOR COEFFICIENT QUANTIZER AND PREDICTION ORDER ESTIMATOR FOR LOSSLESS SPEECH coding

LOW-COMPLEXITY PARCOR COEFFICIENT QUANTIZER AND PREDICTION O...

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

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

ISBN: (纸本)9781424442966

This paper describes two low-complexity tools used for the new ITU-T recommendation G.711.0, the lossless coding 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 Lossless compression Standardization linear predictive coding G.711

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OBJECTIVE QUALITY ASSESSMENT OF SPEECH ENHANCEMENT ALGORITHMS USING BOOTSTRAP-BASED MULTIPLE HYPOTHESES TESTS

OBJECTIVE QUALITY ASSESSMENT OF SPEECH ENHANCEMENT ALGORITHM...

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

作者： Lu, Zhihua Heidenreich, Philipp Zoubir, Abdelhak M. Tech Univ Darmstadt Signal Proc Grp D-64283 Darmstadt Germany

ISBN: (纸本)9781424442966

In this paper bootstrap resampling techniques are applied to assess speech quality and thereby evaluate performance of distinct speech enhancement algorithms, under the assumption that the speech segments can be approximated by an autoregressive model. A bootstrap-based multiple hypotheses testing procedure is constructed to test a distance measure based on linear predictive coding, which is the log-likelihood ratio distance. It is shown that the multiple hypotheses test results correlate well with conventional numerical distance measures, which suggests the applicability of the proposed procedure in assessment of speech quality as well as speech enhancement algorithms.

关键词： Bootstrap speech signal processing linear predictive coding multiple hypothesis testing

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Room Acoustic Response Modeling and Equalization with linear predictive coding and Parametric Filters for Speech and Audio Enhancement

Room Acoustic Response Modeling and Equalization with Linear...

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Asilomar Conference on Signals, Systems & Computers

作者： Sunil Bharitkar Yun Zhang Chris Kyriakakis University of Southern California Los Angeles CA USA

Room acoustic response modeling is a challenging problem. Typical applications include speech dereverberation and loudspeaker correction. Traditionally, infinite-duration impulse response (IIR) or finite-duration impulse response (FIR) filters have been used for acoustic response modeling and equalization. The IIR filter, also called a parametric filter, has a bell-shaped magnitude response and is characterized by its center frequency, the gain at the center frequency, and a Q factor (which is inversely related to the bandwidth of the filter) and is easily implemented as a cascade for purposes of room response modeling and equalization. In this paper we present a technique for determining the coefficients of a second order IIR using a linear predictive coding (LPC) model, where the poles or roots of a high-order LPC dictate the parameters of the parametric filter. Due to the band interactions between the IIR filters, forming the cascade to model the room response, we also present a technique to optimize the Q values so as to better characterize the room response. An accurate model allows for better equalization, for correcting the loudspeaker and room acoustics for speech/audio enhancement, particularly at low frequencies. Alternatively, this technique can be utilized for speech dereverberation applications where the room responses have been estimated a priori. The advantages of the proposed method is the fast computation of the IIR filter parameters, from to the LPC model, since (i) the LPC model is efficient to compute since it uses the Levinson-Durbin recursion to solve the normal equations that arise from the least squares formulation, and (ii) a reasonably high-order LPC model is able to accurately model the low-frequency room response modes.

关键词： predictive models linear predictive coding Nonlinear filters Speech enhancement Speech coding IIR filters Finite impulse response filter Frequency Loudspeakers Q factor

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

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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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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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