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A FREQUENCY-WEIGHTED ITAKURA SPECTRAL DISTORTION MEASURE AND ITS APPLICATION TO SPEECH RECOGNITION IN NOISE

IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1988年第1期36卷 41-48页

作者： SOONG, FK SONDHI, MM AT and T Bell Laboratories Inc. Murray Hill NJ USA

The authors propose an adaptively weighted Itakura distortion measure. They studied its effects on the performance of a conventional dynamic time-warping (DTW)-based speech recognizer in a series of speaker-independent, isolated-digit-recognition experiments. The equivalent SNR improvement achieved by using the proposed weighted Itakura distortion at low SNRs is about 5-7 dB.< >

关键词： Distortion measurement Frequency measurement Speech recognition linear predictive coding Acoustic distortion Gain measurement Noise measurement Testing Signal to noise ratio predictive models

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Design and Performance of an Algorithm for Estimating Vocal System Parameters

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1994年第4期2卷 531-536页

作者： Thomson, Mark M. Guillemin, Bernard J. Univ Auckland Dept Elect & Elect Engn Auckland 1 New Zealand

In this correspondence, we present two findings regarding an algorithm proposed by Milenkovic for determining the source and transfer function of the human vocal system. First, we show that more reliable estimates of the glottal endpoints can be obtained by modifying the procedure used to update the initial endpoint estimates. Second, we show that the algorithm is useful only for analyzing sounds of low to moderate pitch, because of its dependence on an initial transfer function estimate obtained by linear prediction.

关键词： Algorithm design and analysis Parameter estimation Transfer functions Least squares approximation Speech linear predictive coding Shape control Taylor series Polynomials Pulse shaping methods

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Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2011年第4期10卷 225-238页

作者： Istepanian, Robert S. H. Sungoor, Ala Nebel, Jean-Christophe Kingston Univ London Mobile Informat & Network Technol Res Ctr Kingston Upon Thames KT1 2EE Surrey England Kingston Univ London Fac Sci Engn & Comp Kingston Upon Thames KT1 2EE Surrey England

Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.

关键词： Discrete wavelet fractal dimension genomic signal processing linear predictive coding microarray clustering vector quantization

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Nonlinear predictive image coding with a neural network

Nonlinear predictive image coding with a neural network

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

作者： Z. He H. Li DSP Division Radio Engineering Department South-East University Nanjing China

A multilayer neural network used for nonlinear predictive image coding is described. Two coding schemes, nonadaptive and adaptive, are shown. Owing to matching of the local properties of the image, nonlinear predictive coding gives a better performance than linear predictive coding. A series of computer experiments shows the method has not only the ability to generalize but also noise reduction capabilities. Compared with differential pulse code modulation (DPCM), it greatly reduces the number of bits to be transmitted.< >

关键词： Image coding Neural networks Multi-layer neural network Pulse modulation predictive coding linear predictive coding Noise reduction Modulation coding

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The Modular Neural predictive coding architecture

The Modular Neural Predictive Coding architecture

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International Conference on Neural Information Processing

作者： M. Chetouani B. Gas J.L. Zarader Laboratoire des Instruments et Systemes d'Ile de France (LISIF) Universite Paris VI Paris France

ISBN: (纸本)9810475241

We present a new architecture called the Modular Neural predictive coding architecture (Modular NPC). This architecture is used for speech discriminant feature extraction (DFE). We present an application of the modular NPC architecture on phoneme recognition task. The phonemes which are extracted from the Darpa-Timit speech database are: vowels, /b/-/d/-/g/ and /p/-/t/-/k/ phonemes. Comparisons with coding methods (LPC, MFCC, PLP) are presented.

关键词： predictive coding Feature extraction linear predictive coding Speech recognition predictive models Mel frequency cepstral coefficient linearity Optimized production technology Speech processing Vectors

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REDUCTION OF BROAD-BAND NOISE IN SPEECH BY TRUNCATED QSVD

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1995年第6期3卷 439-448页

作者： JENSEN, SH HANSEN, PC HANSEN, SD SORENSEN, JA TECH UNIV DENMARK INST ELECTRDK-2800 LYNGBYDENMARK

We consider an algorithm for reduction of broadband noise in speech based on signal subspaces. The algorithm is formulated by means of the quotient singular value decomposition (QSVD). With this formulation, a prewhitening operation becomes an integral part of the algorithm. We demonstrate that this is essential in connection with updating issues in real-time recursive applications. We also illustrate by examples that we are able to achieve a satisfactory quality of the reconstructed signal.

关键词： Noise reduction Speech enhancement linear predictive coding Acoustic noise Telephony Microphones Noise cancellation Speech analysis Speech synthesis Matrix decomposition

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SHAPE-GAIN MATRIX QUANTIZERS FOR LPC SPEECH

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1986年第6期34卷 1427-1439页

作者： TSAO, C GRAY, RM STANFORD UNIV DEPT ELECT ENGNINFORMAT SYST LABSTANFORDCA 94305

It has been recently demonstrated that the principles of vector quantization for LPC speech can be simply extended to encompass matrices of LPC vectors with significant savings in bit rate. Unfortunately, however, such locally optimal matrix quantizers have prohibitively high complexity and memory requirements when implemented in a speech vocoder at bit rates giving acceptable quality speech. One approach to solving the problem is to separately code gain and shape in the matrix quantizer. This paper generalizes the principles of shape-gain vector quantizer design for LPC speech to matrix quantization and investigates the properties of the resulting quantizers. In particular, we present a design which combines shape matrices consisting of N shape vectors with K-dimensional gain vectors, where N and K are small integers, in practice, with K \geq N . Experimental results show that with K, N \geq 3 , significant reductions in bit rate over locally optimal vector quantizers are obtained for comparable performance. Simulations indicate that a shape-gain matrix quantizer, using a 10 bit shape codebook and an 8 bit codebook with K = N = 3 operating at 6 bits/frame for the LPC model, gives speech quality comparable to a locally optimal vector quantizer at 9 bits/frame. The matrix quantizer has somewhat greater than 5.7 times the memory requirement of the above vector quantizer, but less than 2.1 times the complexity. Subjective tests show that the speech from this matrix quantizer is intelligible to native speakers of English.

关键词： linear predictive coding Speech Shape Bit rate Information systems Signal processing algorithms Testing Government Laboratories

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An alarm method for a Loose Parts Monitoring System

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SHOCK AND VIBRATION 2012年第4期19卷 753-761页

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

In order to reduce the false alarm rate and missed detection rate of a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid method combining linear predictive coding (LPC) and Support Vector Machine (SVM) together to discriminate the loose part signal is proposed. The alarm process is divided into two stages. The first stage is to detect the weak burst signal for reducing the missed detection rate. Signal is whitened to improve the SNR, and then the weak burst signal can be detected by checking the short-term Root Mean Square (RMS) of the whitened signal. The second stage is to identify the detected burst signal for reducing the false alarm rate. Taking the signal's LPC coefficients as its characteristics, SVM is then utilized to determine whether the signal is generated by the impact of a loose part. The experiment shows that whitening the signal in the first stage can detect a loose part burst signal even at very low SNR and thusly can significantly reduce the rate of missed detection. In the second alarm stage, the loose parts' burst signal can be distinguished from pulse disturbance by using SVM. Even when the SNR is -15 dB, the system can still achieve a 100% recognition rate.

关键词： Alarm signal Loose Parts Monitoring System Support Vector Machine linear predictive coding

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Spatially-coupled communication system for the correlated erasure channel

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IET COMMUNICATIONS 2013年第8期7卷 755-765页

作者： Ashrafi, Reza A. Pusane, Ali Emre Bogazici Univ Dept Elect & Elect Engn Istanbul Turkey

Low implementation complexity, low delay and close-to-optimal performance over a wide variety of channels are some of the advantages of spatially-coupled low-density parity-check (LDPC) codes. However, the error performance of the sliding window decoding scheme that is used to decode these codes is considerably degraded over channels with memory, such as the correlated erasure channel. Employing a block interleaver to encounter this situation is not always a viable option, since it introduces a large amount of delay and cancels out the low-delay property of the sliding window decoder. Another way to reduce the effects of erasure bursts is to construct a more robust code ensemble by presenting additional code design rules. However, this approach results in additional constraints on the already complicated code construction process. The authors propose a novel communication system that combats the effects of the erasure bursts through the use of a convolutional interleaver. The proposed system combines the inherent convolutional nature of the spatially-coupled LDPC codes with that of a convolutional interleaver to achieve very low overall delay. The performance of the proposed approach is analysed using the density evolution technique and the performance improvement is demonstrated as a function of the interleaving delay via computer simulations.

关键词： channel coding linear predictive coding parity check codes spatially coupled communication system correlated erasure channel spatially coupled low density parity check codes LDPC codes sliding window decoding scheme block interleaver low delay property linear predictive coding channel coding linear predictive coding parity check codes spatially coupled communication system correlated erasure channel spatially coupled low density parity check codes LDPC codes sliding window decoding scheme block interleaver low delay property linear predictive coding

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Enumeration and Trellis-Searched coding Schemes for Speech LSP Parameters

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1993年第3期1卷 304-314页

作者： Malone, Kevin T. Fischer, Thomas R. Sandia Natl Labs Commun Syst Div Albuquerque NM 87185 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

Speech coders employing forward adaptive predictive coding (APC) and operating at medium-to-low bit rates necessitate efficient encoding of the linear predictive coding (LPC) coefficients. Line spectrum pair (LSP) parameters are currently one of the most efficient choices of transmission parameters for the LPC coefficients. This paper briefly reviews LSP parameters and presents several low delay coding schemes for the parameters. The coders are simulated using data generated from both the autocorrelation and covariance LPC analysis methods. The performances of the coders are given for a variety of rates and LPC analysis conditions. The most efficient scheme developed herein uses a predictive form of trellis coded quantization (TCQ). Its performance is comparable or superior to that of other low delay LSP coding schemes in the literature. An enumeration scheme that reduces the rate of a given scalar quantization structure without decreasing coder performance is also presented.

关键词： Speech coding linear predictive coding Quantization Filters Encoding Delay Decoding Distortion measurement Autocorrelation Reflection

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