This paper incorporates trellis coded vector quantization (TCVQ) and forward adaptive predictive coding (APC) to form an efficient speech coding system operating at bit rates of 16 and 9.6 kb/s. The effectiveness of t...
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This paper incorporates trellis coded vector quantization (TCVQ) and forward adaptive predictive coding (APC) to form an efficient speech coding system operating at bit rates of 16 and 9.6 kb/s. The effectiveness of the system is studied for a variety of system parameters and utterances. Simulation results indicate that segmental signal-to-noise ratios as high as 23.8 and 15.4 dB are obtainable at 16 and 9.6 kb/s, respectively. The quality of the reconstructed speech is deemed to be excellent at 16 kb/s and very good at 9.6 kb/s. An algorithm for "optimizing" the residual codebooks is also presented.
The speech processing studies have advanced rapidly in recent years spurred on by great progresses in the VLSI technologies and in the digitalization of the networks. This paper offers an overview of the most attracti...
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The speech processing studies have advanced rapidly in recent years spurred on by great progresses in the VLSI technologies and in the digitalization of the networks. This paper offers an overview of the most attractive techniques which have focused the recent researchs and developments in speech coding, recognition and synthesis areas. For speech compression, the emphasis is put on a family of techniques named code-excited linear prediction (CELP) which dominates current studies for rates In the range of 4 to 16 kbit/s. In terms of speech recognition, particular emphasis is placed on the following three elements which are essential in order to increase the robustness of the systems : telephone line adaptation, rejection of parasite noise and out-of-vocabulary words, and keyword spotting. In terms of text-to-speech synthesis, the PSOLA (pitch synchronous overlap and add) technique is outlined herein. This technique gives rise to a new generation of synthesis systems which produce speech with very natural timbre. The analysis of current tendencies for each area allows to suggest attractive directions for future research.
We analyze the performance of a CELP coder where the vector quantization (VQ) of the excitation is replaced with trellis-coded vector quantization (TCVQ), Our results show that TCVQ performs significantly better than ...
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We analyze the performance of a CELP coder where the vector quantization (VQ) of the excitation is replaced with trellis-coded vector quantization (TCVQ), Our results show that TCVQ performs significantly better than VQ, with reasonable complexity. This makes TCVQ a fair choice for trading quality against complexity and/or delay, We describe a systematic procedure to replace VQ with TCVQ for existing CELP coders, We propose an optimization algorithm to appropriately populate the trellis, We show how pseudo-Gray coding [22] can be applied to the TCVQ codebook to improve intrinsic coder robustness to channel errors. Finally, we evaluate the complexity and performance of the method.
A data compression technique using a bit-plane decomposition strategy of multivalued images is described, Although the bit-plane decomposition is mainly used for image transmission, our method takes the image expressi...
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A data compression technique using a bit-plane decomposition strategy of multivalued images is described, Although the bit-plane decomposition is mainly used for image transmission, our method takes the image expression for image database into consideration, It has two merits which are a hierarchical representation using depth-first (DF) expression and a simple noise reduction algorithm on the DF expression that is similar to human perception, DF expression is useful for image expansion, rotation, etc. We will study what information in an image should be eliminated as a noise reduction. Noise-like patterns in an image are uniformalized and the edge and smooth surfaces remain nearly unchanged, They are not blurred, but instead are a little enhanced, In this paper, we study a property of black-and-white (B/W) boundary points on bit-planes, The algorithm of the uniformalization process with a DF-expression of an image is described for coding. The experiment for real image data is carried out by a comparison to the other methods, and the results are discussed.
A novel image compression algorithm is presented. The algorithm combines the recursive processing characteristic of predictive coding with the use of a noncausal image model. Experimental results demonstrate the high ...
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A novel image compression algorithm is presented. The algorithm combines the recursive processing characteristic of predictive coding with the use of a noncausal image model. Experimental results demonstrate the high quality of the reconstructed image at a low bit rate (0.375 bit/pixel). This contrasts with the significant loss of detail and blocking artifacts introduced by a JPEG (Joint Photographic Experts Group) type DCT (discrete cosine transform) method at the same bit rate.< >
In this paper a waveform coder configuration based on non linear adaptive prediction will be described. The coder is based on the characteristic of Volterra predictors to model non linear phenomena and to gather infor...
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In this paper a waveform coder configuration based on non linear adaptive prediction will be described. The coder is based on the characteristic of Volterra predictors to model non linear phenomena and to gather informations about the periodicity of the signal via high order statistical moments. The main result is that, by using this type of predictor, lower variance error signals can be obtained, as compared to the classical, linear, case.
The adaptive predictive coding with transform domain quantization (APC-TQ) technique was proposed by Bhaskar (1991) for the compression of audio signals. Since then, significant developments have taken place leading t...
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The adaptive predictive coding with transform domain quantization (APC-TQ) technique was proposed by Bhaskar (1991) for the compression of audio signals. Since then, significant developments have taken place leading to a reduction in the coding rate. While enhancing the audio quality. These developments include (i) the use of block size adaptation to exploit the variations in the stationarity of the signal, (ii) high resolution spectral modeling using LPC analysis orders up to 64, and (iii) an adaptive bit-allocation procedure to minimize coding noise power as well as minimize the perception of coding noise. The result is a near transparent quality compression of 5 kHz bandwidth audio at a rate of 17 kbit/s. This technology will find applications in the distribution and transmission of AM quality audio programming over low rate channels such as the INMARSAT Standard A, B and aeronautical systems.< >
The paper describes the application of adaptive filters in a two stage lossless data compression algorithm. The term lossless implies that the original data can be recovered exactly. The first stage of the scheme cons...
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The paper describes the application of adaptive filters in a two stage lossless data compression algorithm. The term lossless implies that the original data can be recovered exactly. The first stage of the scheme consists of a lossless adaptive predictor while the second stage performs arithmetic coding. The unique aspects of the paper are: (a) defining the concept of a reversible filter as opposed to an invertible filter; (b) performing lossless data compression using primarily floating-point operations; (c) designing lossless adaptive predictors; (d) using a modified arithmetic coding algorithm that can readily handle inputs consisting of more than 14 bits.
A generic nonlinear autoregressive (AR) model for a random time series is presented. The model is obtained by a nonlinear predictive coding (NLPC) approach which expresses the minimum mean square error estimate of the...
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A generic nonlinear autoregressive (AR) model for a random time series is presented. The model is obtained by a nonlinear predictive coding (NLPC) approach which expresses the minimum mean square error estimate of the current value of the series as a Volterra series in terms of its immediate N preceding values. This Volterra series is assumed to belong to a generalized Fock Hilbert space F. In the second stage, which is parametric, the model parameters, which are coefficients of a linear combination of known nonlinear random functions of the data, are obtained by linear mean square estimation. The implementations of the model and of the estimator appear respectively as two layer recurrent and feedforward neural networks.< >
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