Here we consider the problem of providing near optimal performance for a large set of possible models. We adopt the H ∞ framework in the single-input single-output (SISO) setting with structured uncertainty: a compac...
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Here we consider the problem of providing near optimal performance for a large set of possible models. We adopt the H ∞ framework in the single-input single-output (SISO) setting with structured uncertainty: a compact set of controllable and observable plant models of a fixed order; we consider the control problem of designing a controller to minimize the worst case performance. We consider two different feedback configurations, and under a mild assumption we prove that a linear periodic controller (LPC) exists which achieves the objective.
Vector quantization (VQ) is examined as a technique to enhance performance in subband coding of speech at 9.6 kb/s. The set of short-term subband power levels is vector quantized, providing low-rate side information t...
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Vector quantization (VQ) is examined as a technique to enhance performance in subband coding of speech at 9.6 kb/s. The set of short-term subband power levels is vector quantized, providing low-rate side information to control the coding of the subband signals. Each subband signal is then vector quantized with variable size codebooks that are dynamically assigned by the quantized side information. Two versions are described, a 7-band coder and a 14-band coder. Simulation results demonstrate that vector quantization offers a distinct perceptual improvement compared with scalar quantization of the same subband signals and side information for the same total bit rate.
In this paper, we revisit the manifold assumption which has been widely adopted in the learning-based image super-resolution. The assumption states that point-pairs from the high-resolution manifold share the local ge...
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In this paper, we revisit the manifold assumption which has been widely adopted in the learning-based image super-resolution. The assumption states that point-pairs from the high-resolution manifold share the local geometry with the corresponding low-resolution manifold. However, the assumption does not hold always, since the one-to-multiple mapping from LR to HR makes neighbor reconstruction ambiguous and results in blurring and artifacts. To minimize the ambiguous, we utilize Locality Preserving Constraints (LPC) to avoid confusions through emphasizing the consistency of localities on both manifolds explicitly. The LPC are combined with a MAP framework, and realized by building a set of cell-pairs on the coupled manifolds. Finally, we propose an energy minimization algorithm for the MAP with LPC which can reconstruct high quality images compared with previous methods. Experimental results show the effectiveness of our method.
The ARMA model provides an effective means for precise representation of the speech production process. A stable and accurate estimation of ARMA parameters from the speech signal has been shown to be possible by the S...
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The ARMA model provides an effective means for precise representation of the speech production process. A stable and accurate estimation of ARMA parameters from the speech signal has been shown to be possible by the SEARMA method. In this paper we propose an ARMA speech analysis-synthesis system based on the SEARMA method. The validity of the proposed system is investigated by means of both objective and subjective evaluation. The results reveal that the zeros in the spectrum contribute to the reduction of spectral distortion and to the improvement of the quality of synthetic speech.
作者:
P. PrandomM. GoodwinM. VetterliLCAV
Ecole Polytech. Fed. de Lausanne Switzerland EECS
University of California Berkeley USA LCAV
Ecole Polytechnique Fédérale de Lausanne Switzerland
The idea of optimal joint time segmentation and resource allocation for signal modeling is explored with respect to arbitrary segmentations and arbitrary representation schemes. When the chosen signal modeling techniq...
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The idea of optimal joint time segmentation and resource allocation for signal modeling is explored with respect to arbitrary segmentations and arbitrary representation schemes. When the chosen signal modeling techniques can be quantified in terms of a cost function which is additive over distinct segments, a dynamic programming approach guarantees the global optimality of the scheme while keeping the computational requirements of the algorithm sufficiently low. Two immediate applications of the algorithm to LPC speech coding and to sinusoidal modeling of musical signals are presented.
In this paper, we introduce an auto-regressive moving average (ARMA) lattice model for speech modeling. The speech characteristics are modeled and expressed in the form of lattice reflection coefficients for classific...
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In this paper, we introduce an auto-regressive moving average (ARMA) lattice model for speech modeling. The speech characteristics are modeled and expressed in the form of lattice reflection coefficients for classification. Self Organization Map (SOM) is used to build codebooks for classification and recognition of the lattice reflection coefficients. Experimental results based on an isolated word speech database of 10 words/names indicate that the ARMA lattice model achieves superior recognition performance as compared to those of the conventional auto-regressive (AR) model.
In speech coding, segment vocoders offer good intelligibility at low bit rates. A segment vocoder has four basic components 1) Segmentation of input speech 2) Segment quantization 3) Residual quantization 4) Synthesis...
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ISBN:
(纸本)9781424463831;9781424463855
In speech coding, segment vocoders offer good intelligibility at low bit rates. A segment vocoder has four basic components 1) Segmentation of input speech 2) Segment quantization 3) Residual quantization 4) Synthesis of speech. Most segment vocoders use a recognition approach to segment quantization. In this paper, we assume a different approach to segment quantization. The segmental unit is a syllable and the segment codebook stores the sequence of LPC vectors. During the encoding process the speech segment is quantized using the sequence of LPC vectors that result in the smallest residual energy. PESQ scores indicate that this vocoder achieves better quality compared to that of a corresponding vocoder that uses a speech recognition framework.
Summary form only given. A data compression technique has been developed for efficient communication and storage of seismic waveform data. The technique is also useful with similar kinds of continuous waveform data. I...
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Summary form only given. A data compression technique has been developed for efficient communication and storage of seismic waveform data. The technique is also useful with similar kinds of continuous waveform data. It uses a special version of the linear predictive coding method, along with secondary bi-level sequence coding. The principal feature of this two-stage technique is that it provides exact, bit-for-bit recovery of the original data.< >
linear prediction method is one of the most frequently used analysis methods of speech. Covariance method and auto-correlation method of linear prediction often fail to make a precise analysis of speech because of the...
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linear prediction method is one of the most frequently used analysis methods of speech. Covariance method and auto-correlation method of linear prediction often fail to make a precise analysis of speech because of the excitation source or fundamental frequency. In order to decrease the affect of the excitation source, various kinds of difference operations are usually employed for preprocessing. However, such preprocessings do not always work satisfactorily. Here proposed is a new approach to LPC analysis based on selective use of speech data to reject the data disturbed by the excitation source, and is called selective linear prediction method. The method is constructed aiming to improve the accuracy of analysis. First, the formulation of linear prediction is presented using generalized inverse matrices. Then, a successive computation is described based on Givens' reduction. The selective computation, which plays an essential role in our method, owes its efficiency to Givens' reduction. Finally the advantage of the proposed method is demonstrated by computer simulation using both synthetic and natural speech.
In this paper, we propose a pitch synchronous addition method for LPC analysis by making use of the periodicity of speech. It is shown that the solution overcomes the difficulty involved with the technique of noise re...
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In this paper, we propose a pitch synchronous addition method for LPC analysis by making use of the periodicity of speech. It is shown that the solution overcomes the difficulty involved with the technique of noise reduction compatible with the stability of the LPC filter obtained by subtracting the noise part from the autocorrelation function of speech. The relation between the pitch period of speech and the improvement in signal-to-noise ratio accomplished by the method is investigated. The simulation results show the effectiveness of the proposed method especially for high-pitched speech.
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