The Experts below are selected from a list of 83280 Experts worldwide ranked by ideXlab platform
Franz Hlawatsch - One of the best experts on this subject based on the ideXlab platform.
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Bilinear Signal Synthesis
IEEE Transactions on Signal Processing, 1992Co-Authors: Franz Hlawatsch, W. KrattenthalerAbstract:The authors discuss the Signal Synthesis problem in the general framework of bilinear Signal representations (BSRs), thereby obtaining a unified treatment which encompasses, e.g., the Wigner distribution and ambiguity function as special cases. The inclusion of a Signal space constraint serves to impart flexibility to the Signal Synthesis process and to relax mathematical requirements. The characterization of Signal spaces either by orthogonal projection operators or by orthonormal bases leads to two different Signal Synthesis methods. Both methods assume the BSR to be unitary (i.e., satisfy Moyal's formula) on the Signal space on which Signal Synthesis is performed. As an application of the general Signal Synthesis methods, band-limited Signal Synthesis in the case of the Wigner distribution is considered. >
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Regularity and unitarity of bilinear time-frequency Signal representations
IEEE Transactions on Information Theory, 1992Co-Authors: Franz HlawatschAbstract:Two structural properties of bilinear time-frequency representations (BTFRs) of Signals are introduced and studied. The definition of these properties is based on a linear-operator description of BTFRs. The first property, termed regularity, has important implications with respect to the recovery of Signals from the BTFR outcome, the derivation of other bilinear Signal representations from a BTFR, the BTFRs reaction to linear Signal transformations, and the construction of bases of induced BTFR-domain spaces. The second property, called unitarity, is equivalent to validity of Moyal's formula (1949). Unitarity is thus necessary and sufficient for a closed-form solution of optimal Signal Synthesis and for a BTFR formulation or optimal detection/estimation methods. Unitarity also allows the systematic construction of BTFR product relations like the Wigner distribution's interference formula and the ambiguity function's self-transformation property. Unitarity permits the construction of induced orthogonal projection operators and guarantees the orthonormality of induced basis functions. >
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ICASSP - Two Signal Synthesis algorithms for pseudo Wigner distribution
ICASSP-88. International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: W. Krattenhaler, Franz HlawatschAbstract:The pseudo-Wigner distribution (PWD) is a time-frequency Signal representation particularly suited for analyzing and processing 'long' Signals. Signal processing by means of PWD involves a Signal Synthesis step. Two Signal Synthesis algorithms for PWD are presented. These are the pseudopower method which allows optimal Signal Synthesis, but is computationally expensive for longer Signals, and the partial sum method, which is suboptimal but suited for the Synthesis of Signals with arbitrary length. The performance of the two algorithms is demonstrated by simple Synthesis experiments. >
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ICASSP - Time-frequency Signal Synthesis on Signal subspaces
ICASSP '87. IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Franz Hlawatsch, W. KrattenthalerAbstract:Signal Synthesis is an indispensable part of Signal processing schemes based on bilinear time-frequency representations like Wigner distribution, ambiguity function or spectrogram. We present a comprehensive theory of Signal Synthesis in which a Signal subspace constraint imparts flexibility to the Synthesis process. Representing the Signal subspace by a projection operator or an explicit basis results in two different types of Synthesis algorithms. Subspace Synthesis is particularly suited for Wigner distribution and ambiguity function.
Anders Gunnarsson - One of the best experts on this subject based on the ideXlab platform.
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music Signal Synthesis using sinusoid models and sliding window esprit
International Conference on Multimedia and Expo, 2006Co-Authors: Anders Gunnarsson, Irene GuAbstract:This paper proposes a music Signal Synthesis scheme that is based on sinusoid modeling and sliding-window ESPRIT. Despite widely used audio coding standards, effectively synthesizing music using sinusoid models, more suitable for harmonic rich music Signals, remains an open issue. In the proposed scheme, music Signals are modeled by a sum of damped sinusoids in noise. A sliding window ESPRIT algorithm is applied. A continuity constraint is then imposed for tracking the time trajectories of sinusoids in music and for removing spurious spectral peaks in order to adapt to the changing number of sinusoid contents in dynamic music. Simulations have been performed to several music Signals with a range of complexities, including music recorded from banjo, flute and music with mixed instruments. The results from listening and spectrograms have strongly indicated that the proposed method is very robust for music Synthesis with good quality.
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ICME - Music Signal Synthesis using Sinusoid Models and Sliding-Window Esprit
2006 IEEE International Conference on Multimedia and Expo, 2006Co-Authors: Anders GunnarssonAbstract:This paper proposes a music Signal Synthesis scheme that is based on sinusoid modeling and sliding-window ESPRIT. Despite widely used audio coding standards, effectively synthesizing music using sinusoid models, more suitable for harmonic rich music Signals, remains an open issue. In the proposed scheme, music Signals are modeled by a sum of damped sinusoids in noise. A sliding window ESPRIT algorithm is applied. A continuity constraint is then imposed for tracking the time trajectories of sinusoids in music and for removing spurious spectral peaks in order to adapt to the changing number of sinusoid contents in dynamic music. Simulations have been performed to several music Signals with a range of complexities, including music recorded from banjo, flute and music with mixed instruments. The results from listening and spectrograms have strongly indicated that the proposed method is very robust for music Synthesis with good quality.
Werner Kozek - One of the best experts on this subject based on the ideXlab platform.
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Time-frequency projection filters and time-frequency Signal expansions
IEEE Transactions on Signal Processing, 1994Co-Authors: F. Hlawatsch, Werner KozekAbstract:We consider the problems of designing a linear, time-varying filter with a specified "time-frequency (TF) pass region" and of constructing an orthonormal basis for the parsimonious expansion of Signals located in a given TF support region. These problems of TF filtering and TF Signal expansion are reduced to the problem of designing a "TF subspace", i.e., a linear Signal space comprising all Signals located in a given TF legion. Specifically, the TF filter is taken to be the orthogonal projection operator on the TF subspace. We present an optimum design of TF subspaces that is based on the Wigner distribution of a linear Signal space and is an extension of the well-known Signal Synthesis problem. The optimum TF subspace is shown to be an "eigenspace" of the TF region, and some properties of eigenspaces are discussed. The performance of TF projection filters and TF Signal expansions is studied both analytically and via computer simulation. >
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Time-frequency Signal processing based on the Wigner-Weyl framework
Signal Processing, 1992Co-Authors: Werner KozekAbstract:Abstract In this paper, a new approach to time-frequency Signal processing is presented. We study the design of linear time-varying systems with regard to time-frequency filtering. The particular problem addressed is the separation of non-stationary Signals with disjoint time-frequency support. As underlying time-frequency Signal representation we choose the Wigner distribution. We show that the Weyl correspondence allows a simple and effective design of linear time-frequency filters. The proposed method of linear filtering turns out to have both superior performance and reduced cost compared to the nonlinear Signal Synthesis method.
Irene Gu - One of the best experts on this subject based on the ideXlab platform.
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music Signal Synthesis using sinusoid models and sliding window esprit
International Conference on Multimedia and Expo, 2006Co-Authors: Anders Gunnarsson, Irene GuAbstract:This paper proposes a music Signal Synthesis scheme that is based on sinusoid modeling and sliding-window ESPRIT. Despite widely used audio coding standards, effectively synthesizing music using sinusoid models, more suitable for harmonic rich music Signals, remains an open issue. In the proposed scheme, music Signals are modeled by a sum of damped sinusoids in noise. A sliding window ESPRIT algorithm is applied. A continuity constraint is then imposed for tracking the time trajectories of sinusoids in music and for removing spurious spectral peaks in order to adapt to the changing number of sinusoid contents in dynamic music. Simulations have been performed to several music Signals with a range of complexities, including music recorded from banjo, flute and music with mixed instruments. The results from listening and spectrograms have strongly indicated that the proposed method is very robust for music Synthesis with good quality.
W. Krattenthaler - One of the best experts on this subject based on the ideXlab platform.
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Bilinear Signal Synthesis
IEEE Transactions on Signal Processing, 1992Co-Authors: Franz Hlawatsch, W. KrattenthalerAbstract:The authors discuss the Signal Synthesis problem in the general framework of bilinear Signal representations (BSRs), thereby obtaining a unified treatment which encompasses, e.g., the Wigner distribution and ambiguity function as special cases. The inclusion of a Signal space constraint serves to impart flexibility to the Signal Synthesis process and to relax mathematical requirements. The characterization of Signal spaces either by orthogonal projection operators or by orthonormal bases leads to two different Signal Synthesis methods. Both methods assume the BSR to be unitary (i.e., satisfy Moyal's formula) on the Signal space on which Signal Synthesis is performed. As an application of the general Signal Synthesis methods, band-limited Signal Synthesis in the case of the Wigner distribution is considered. >
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ICASSP - Time-frequency Signal Synthesis on Signal subspaces
ICASSP '87. IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Franz Hlawatsch, W. KrattenthalerAbstract:Signal Synthesis is an indispensable part of Signal processing schemes based on bilinear time-frequency representations like Wigner distribution, ambiguity function or spectrogram. We present a comprehensive theory of Signal Synthesis in which a Signal subspace constraint imparts flexibility to the Synthesis process. Representing the Signal subspace by a projection operator or an explicit basis results in two different types of Synthesis algorithms. Subspace Synthesis is particularly suited for Wigner distribution and ambiguity function.