Wavelet packet filterbanks for low time delay audio coding

Philippe, Pierrick; Saint-Martin, F. Moreau de; Lever, M.

doi:10.1109/89.759039

Cited by 42 publications

(12 citation statements)

References 27 publications

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“…Furthermore, even the same material loose particles present different time-domain characteristics with regard to different shapes, sizes and weights. but also processes high-frequency wavelet coefficients subspace (Deng and Ling, 1999;Carnero and Drygajlo, 1999;Linkens et al, 2000;Philippe et al, 1999), suitable for processing a particle signal with wide energy distribution. To obtain more material information about the particle signal, wavelet packet transform instead of Fourier transform is employed to extract eigenvectors in this paper.…”

Section: Eigenvector Extractionmentioning

confidence: 99%

Detection and material identification of loose particles inside the aerospace power supply via stochastic resonance and LVQ network

Wang

Chen

Zhang

et al. 2011

Transactions of the Institute of Measurement and Control

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The detection of loose particles inside an aerospace power supply is important to improve the reliability of the whole space system. This paper investigates the detection and material identification of loose particles within an aerospace power supply based on the particle impact noise detection (PIND) test. A stochastic resonance algorithm is employed to detect the presence of tiny particles. A learning vector quantization (LVQ)-based material identification method is proposed. Finally, experiments are conducted to demonstrate the effectiveness of the proposed technique. Experimental results show that the accuracies of particle detection and material identification are above 90% and 80%, respectively, which meets end-user requirements.

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Section: Eigenvector Extractionmentioning

confidence: 99%

Detection and material identification of loose particles inside the aerospace power supply via stochastic resonance and LVQ network

Wang

Chen

Zhang

et al. 2011

Transactions of the Institute of Measurement and Control

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“…In [13], it has been shown that the bit-rate needed for subband k (assuming a Laplacian subband signal with parameter ak) can be estimated as the entropy which results in -i: Pj log9 p = -(1 -akk/2) log2(1 -kk/2) _ kk/2 log2 sinh YkIk 2 log 2Sinh jHence the bit-rate resulting from uniform quantization followed by entropy coding can be approximated by…”

Section: Bit Allocationmentioning

confidence: 99%

<title>Audio coding based on rate distortion and perceptual optimization</title>

Erne

Moschytz

2000

SPIE Proceedings

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The time-frequency tiling, bit allocation and the quantizer of most perceptual coding algorithms is either fixed or controlled by a perceptual model. The large variety of existing audio signals, each exhibiting different coding requirements due to their different temporal and spectral fine-structure suggests to use a signal-adaptive algorithm. The framework which is described in this paper makes use of a signal-adaptive wavelet filterbank which allows to switch any node of the wavelet-packet tree individually. Therefore each subband can have an individual timesegmentation and the overall time-frequency tiling can be adapted to the signal using optimization techniques. A rate-distortion optimality can be defined which will minimize the distortion for a given rate in every subband, based on a perceptual model. Due to the additivity of the rate and distortion measure over disjoint covers of the input signal, an overall cost function including the switching cost for the filterbank switching can be defined. By the use of dynamic programming techniques, the wavelet-packet tree can be pruned based on a top-down or bottom-up "split-merge" decision in every node of the wavelet-tree. Additionally we can profit from temporal masking due to the fact that each subband can have an individual segmentation in time without introducing time domain artifacts such as pre-echo distortion.

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“…Many signals in areas like music, speech and images can be efficiently represented by wavelets. Wavelet packets, owing to their nice characteristics, have been widely applied to signal processing [4], code theory, image compression , solving integral equation and so on. Coifman and Meyer firstly introduced the notion of univariate orthogonal wavelet packets.…”

Section: Introductionmentioning

confidence: 99%

The Properties of Biorthogonal Vector-valued Bivariate Wavelet Packets

Wang

2009

2009 WRI Global Congress on Intelligent Systems

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In this paper, we introduce a class of vector-valued wavelet packets of space L 2 (R 2 ) u , which are generalizations of bivariable wavelet packets. A procedure for constructing a class of biorthogonal vector-valued wavelet packets in higher dimensions is presented and their biorthogonality properties are characterized by virtue of matrix theory, time-frequency analysis method and operator theory. Three biorthogonality formulas regarding these wavelet packets are derived. Furthermore, it is shown how to obtain new Riesz bases of space L 2 (R 2 ) u from these wavelet packets.

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Wavelet packet filterbanks for low time delay audio coding

Cited by 42 publications

References 27 publications

Detection and material identification of loose particles inside the aerospace power supply via stochastic resonance and LVQ network

Detection and material identification of loose particles inside the aerospace power supply via stochastic resonance and LVQ network

<title>Audio coding based on rate distortion and perceptual optimization</title>

The Properties of Biorthogonal Vector-valued Bivariate Wavelet Packets

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