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Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure
 in Proc. IEEE Int. Symp. on Information Theory, p. 55
, 1997
"... Gersho's bounds on the asymptotic performance of vector quantizers are valid for vector distortions which are powers of the Euclidean norm. Yamada, Tazaki and Gray generalized the results to distortion measures that are increasing functions of the norm of their argument. In both cases, the distortio ..."
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Cited by 28 (3 self)
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Gersho's bounds on the asymptotic performance of vector quantizers are valid for vector distortions which are powers of the Euclidean norm. Yamada, Tazaki and Gray generalized the results to distortion measures that are increasing functions of the norm of their argument. In both cases, the distortion is uniquely determined by the vector quantization error, i.e., the Euclidean difference between the original vector and the codeword into which it is quantized. We generalize these asymptotic bounds to inputweighted quadratic distortion measures, a class of distortion measure often used for perceptually meaningful distortion. The generalization involves a more rigorous derivation of a fixed rate result of Gardner and Rao and a new result for variable rate codes. We also consider the problem of source mismatch, where the quantizer is designed using a probability density different from the true source density. The resulting asymptotic performance in terms of distortion increase in dB is shown...
Entropybased distortion measure and bit allocation for wavelet image compression
"... Quality criteria for image coding are often based on mean square error. However, this is not always a relevant measure of visual quality at low bitrate. We investigate here the properties of a distortion measure based on the conditional differential entropy of the input signal given its quantized v ..."
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Quality criteria for image coding are often based on mean square error. However, this is not always a relevant measure of visual quality at low bitrate. We investigate here the properties of a distortion measure based on the conditional differential entropy of the input signal given its quantized value. The proposed measure appears to be a correct representation of the amount of information lost by quantization. An adaptive bit allocation algorithm is proposed in order to take advantage of this criterion. Experimental results illustrate the behavior of the proposed distortion measure and exhibit interesting visual properties for low bitrate subband image coding. Entropy, distortion, image coding.
Speech Analyzer Using a Joint Estimation Model of Spectral Envelope and Fine Structure
"... We have been working on a new speech analyzer based on a parametric representation of speech governed by the F0 parameter, towards practical humanmachine interfaces. As a precise estimation of the frequency response of the vocal tract from a real speech signal requires the power of each component o ..."
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Cited by 1 (1 self)
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We have been working on a new speech analyzer based on a parametric representation of speech governed by the F0 parameter, towards practical humanmachine interfaces. As a precise estimation of the frequency response of the vocal tract from a real speech signal requires the power of each component of the harmonic structure to be accurately estimated, one hopes to have a highprecision estimation of F0. At the same time, under the empirical constraint that speech spectral envelopes are usually smooth in the power domain, half pitch errors can be significantly avoided. Therefore, F0 and the envelope should be estimated jointly rather than separately through an optimal estimation of the spectral envelope and the spectral fine structure. In this article, we introduce a new speech analysis method using a spectral model with a composite function of envelope and fine structure models. Index Terms: parametric speech analyzer, speech synthesis, pitch estimation, spectral envelope estimation.
The author’s work in speech was partially supported by the National Science Foundation. Thanks to J. D.
"... Packet speech on the Arpanet: A history of early LPC speech and its accidental impact on the ..."
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Packet speech on the Arpanet: A history of early LPC speech and its accidental impact on the
California Coding: Early LPC Speech in Santa Barbara,
, 2004
"... Optimal 1step prediction ¿ What is the optimal predictor of the form ˜ Xm = p(X0,..., Xm−1)? Optimal 1step linear prediction ¿ What is the optimal linear predictor of the form ˜ Xm = − � m l=1 alXm−l? Modeling/density estimation ¿ What is the probability density function (pdf) that “best ” model ..."
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Optimal 1step prediction ¿ What is the optimal predictor of the form ˜ Xm = p(X0,..., Xm−1)? Optimal 1step linear prediction ¿ What is the optimal linear predictor of the form ˜ Xm = − � m l=1 alXm−l? Modeling/density estimation ¿ What is the probability density function (pdf) that “best ” models X m? Spectrum Estimation ¿ What is the “best ” estimate of the power spectral density or covariance of the underyling random process? California Coding 3 The Application Speech Coding ¿ How apply linear prediction to produce low bit rate speech of sufficient quality for speech understanding and speaker recognition? Wide literature exists on all of these topics in a speech context and they are intimately related. See, e.g., J. Makhoul’s classic survey [35] and J.D. Markel and A.H. Gray’s classic book [41]. Clearly problems illposed unless define terms like “optimal” and assume some structure.
Performance Evaluation of Blind Equalization for MelLPC based Speech Recognition under Different Noisy Conditions M. Babul Islam
"... This study is aimed to develop a noise robust distributed speech recognizer (DSR) for realworld applications by employing Blind Equalization (BEQ) for robust feature extraction. The main focus of the work is to cope with different noisy environments in recognition phase. To realize this objective, ..."
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This study is aimed to develop a noise robust distributed speech recognizer (DSR) for realworld applications by employing Blind Equalization (BEQ) for robust feature extraction. The main focus of the work is to cope with different noisy environments in recognition phase. To realize this objective, MelLP based speech analysis has been used in speech coding on the linear frequency scale by applying a firstorder allpass filter instead of a unit delay. Mismatch between training and test phases is reduced through robust feature extraction and this is achieved by applying BEQ on MelLP cepstral coefficients as an effort to reduce additive noise and channel distortion. The performance of the proposed system has been evaluated on test set A and set C of Aurora2 database. The baseline performance, that is, for MelLPC the average word accuracy has found to be 59.05 % and 63.99% for sets A and C, respectively. By applying the BEQ on MelLP cepstral coefficients, the performance has been improved to 65.66 % and 64.65 % for sets A and C, respectively.