A nonuniform sampling method of speech signal and its application to speech coding

Rheem, Jae Yeol; Kim, BeomHun; Ann, Souguil

doi:10.1016/0165-1684(94)00089-i

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Under such circumstances sampling should be performed at uneven time intervals, for example: by adaptive sampling, in which the actual sampling moment depends on the previous sample value. Uneven and adaptive sampling methods were described in [2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Identification of fast-changing signals by means of adaptive chaotic transformations

Berezowski

Lawnik

2014

NAMC

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The adaptive approach of strongly non-linear fast-changing signals identification is discussed. The approach is devised by adaptive sampling based on chaotic mapping "in yourself" of a signal. Presented sampling way may be utilized online in the automatic control of chemical reactor (throughout identification of concentrations and temperature oscillations in real-time), in medicine (throughout identification of ECG and EEG signals in real-time), etc. In this paper, we presented it to identify the Weierstrass function and ECG signal.

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Section: Introductionmentioning

confidence: 99%

Identification of fast-changing signals by means of adaptive chaotic transformations

Berezowski

Lawnik

2014

NAMC

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“…We then use the representation given by irregularly spaced MSM samples to build a waveform speech coder. Irregular sampling for waveform speech coding has been used in [14,15]. They both take local extrema of the speech signal as irregularly spaced samples (hence, contrary to the MSM, the size of their sampling is constant and is not controllable) and reconstruction is done either through sinusoidal interpolation [14], local polynomial interpolation or cubic spline interpolation [15].…”

Section: Introductionmentioning

confidence: 99%

“…Irregular sampling for waveform speech coding has been used in [14,15]. They both take local extrema of the speech signal as irregularly spaced samples (hence, contrary to the MSM, the size of their sampling is constant and is not controllable) and reconstruction is done either through sinusoidal interpolation [14], local polynomial interpolation or cubic spline interpolation [15]. In this paper, In order to show the potential of our methodology in waveform speech coding, we use a very simple quantization and encoding of the MSM to develop a fully operational waveform coder and we compare it to the standard G.726 ADPCM [16] waveform coder.…”

Section: Introductionmentioning

confidence: 99%

Non-linear speech representation based on local predictability exponents

et al. 2014

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Looking for new perspectives to analyze non-linear dynamics of speech, this paper presents a novel approach based on a microcanonical multiscale formulation which allows the geometric and statistical description of multiscale properties of the complex dynamics. Speech is a complex system whose dynamics can be, to some extent, geometrically and statistically accessed by the computation of Local Predictability Exponents (LPEs) unlocking the determination of the most informative subset (Most Singular Manifold or MSM), leading to associated compact representation and reconstruction. But the complex intertwining of different dynamics in speech (added to purely turbulent descriptions) suggests the definition of appropriate multiscale functionals that might influence the evaluation of LPEs, hence leading to more compact MSM. Consequently, by using the classical and generic Sauer/Allebach algorithm for signal reconstruction from irregularly spaced samples, we show that speech reconstruction of good quality can be achieved using MSM of low cardinality. Moreover, in order to further show the potential of the new methodology, we develop a simple and efficient waveform coder which achieves almost the same level of perceptual quality as a standard coder, while having a lower bit-rate.

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Signal recovery algorithm for 2-level amplitude sampling using chromatic signal approximations

2018

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A nonuniform sampling method of speech signal and its application to speech coding

Cited by 5 publications

References 5 publications

Identification of fast-changing signals by means of adaptive chaotic transformations

Identification of fast-changing signals by means of adaptive chaotic transformations

Non-linear speech representation based on local predictability exponents

Signal recovery algorithm for 2-level amplitude sampling using chromatic signal approximations

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