The Experts below are selected from a list of 54 Experts worldwide ranked by ideXlab platform

Remi Gribonval - One of the best experts on this subject based on the ideXlab platform.

  • sparse decomposition of stereo signals with matching pursuit and application to blind separation of more than two sources from a stereo mixture
    International Conference on Acoustics Speech and Signal Processing, 2002
    Co-Authors: Remi Gribonval
    Abstract:

    We develop a method of sparse decomposition of stereo audio signals, and test its application to blind separation of more than two sources from only two linear mixtures. The decomposition is done in a stereo Dictionary which we can define based on any standard time-frequency or time-scale Dictionary, such as the multiscale Gabor Dictionary. A decomposition of a stereo mixture in the Dictionary is computed with a Matching Pursuit type algorithm called Stereo Matching Pursuit. We experiment an application to blind source separation with three (mono) sources mixed on two channels. We cluster the parameters of the stereo atoms of the decomposition to estimate the mixing parameters, and recover estimates. of the sources by a partial reconstruction using only the appropriate atoms of the decomposition. The method outperforms the best achievable linear demixing by 3 dB to more than 7 dB on our preliminary experiments, and its performance should increase as we let the number of iterations of the pursuit increase. Sample sound files can be found here: http://www.irisa.fr/metiss/gribonva/

C Jay C Kuo - One of the best experts on this subject based on the ideXlab platform.

  • sparse music representation with source specific dictionaries and its application to signal separation
    IEEE Transactions on Audio Speech and Language Processing, 2011
    Co-Authors: Namgook Cho, C Jay C Kuo
    Abstract:

    We propose a source-specific Dictionary approach to efficient music representation, and apply it to separation of music signals that coexist with background noise such as speech or environmental sounds. The basic idea is to determine a set of elementary functions, called atoms, that efficiently capture music signal characteristics. There are three steps in the construction of a source-specific Dictionary. First, we decompose basic components of musical signals (e.g., musical notes) into a set of source-independent atoms (i.e., Gabor atoms). Then, we prioritize these Gabor atoms according to their approximation capability to music signals of interest. Third, we use the prioritized Gabor atoms to synthesize new atoms to build a compact Dictionary. The number of atoms needed to represent music signals using the source-specific Dictionary is much less than that of the Gabor Dictionary, resulting in a sparse music representation. For the single-channel music signal separation, we project the mixture signal onto source-specific atoms. Experimental results are given to demonstrate the efficiency and applications of the proposed approach.

T M Mcginnity - One of the best experts on this subject based on the ideXlab platform.

  • feature extraction from the eeg for a brain computer interface using genetic matching pursuit algorithm with Gabor Dictionary
    IEEE System Man and Cybernetics UK-RI 2004, 2004
    Co-Authors: Pawel Herman, Girijesh Prasad, T M Mcginnity
    Abstract:

    The electroencephalogram (EEG) isconsidered to be one of the most scientifically exploitedsignals recorded from a human's organism. The difficultyencountered in the processing of the EEG signal stemsfrom its highly non-stationary and complex nature. Itappears that time-frequency (t-f) analysis of theelectroencephalogram is one of the most popularapproaches to the extraction of knowledge about braindynamics. This paper presents the Matching Pursuit (MP)method used for obtaining t-f features from braincomputerinterface (BCl) data, in this case the EEGsignals recorded from a subject performing imaginationof left and right hand movement. The emphasis in thepaper is put on an effective realization of MP with GaborDictionary due to its intensive computational load.Genetic Algorithms (GAs) have been utilized to optimizethe performance of the t-f method under consideration,which has resulted in the implementation of a GeneticMatching Pursuit Algorithm (GMPA). The BCI data areclassified using linear discriminant analysis (LDA) basedon the set of features extracted with the help of theGMPA. The applicability of the technique to a BCI systemis verified on the basis of the classification accuracy (CA)rate.

Aldo Morales - One of the best experts on this subject based on the ideXlab platform.

  • optimized search over the Gabor Dictionary for note decomposition and recognition
    Journal of The Franklin Institute-engineering and Applied Mathematics, 2007
    Co-Authors: Sedig Agili, David B Bjornberg, Aldo Morales
    Abstract:

    Performing real-time note detection of multiple audio sources, such as guitar signals, is a difficult task due to the complex nature of these signals. For instance, guitar notes are close in pitch and the harmonic overtones of each note are strongly interlaced, thus preventing standard filtering techniques. In order to accomplish note detection, a harmonic matching pursuit algorithm has been used to decompose an audio signal in terms of elementary waveforms called harmonic atoms. These atoms are derived from the standard matching pursuit algorithm, and are part of an extended and overcomplete Gabor Dictionary. In this paper, the search over Gabor Dictionary is optimized by using signal modeling of the guitar signal in order to pre-calculate a parameter set; therefore avoiding a costly search over the extended Gabor Dictionary. The parameter set defined for the proposed algorithm includes time-location, decay rate, frequency, scale and phase, which are calculated at the onset of each note played. This optimized algorithm is demonstrated through synthesized and real guitar signal examples. Considerable computational savings of this proposed algorithm over the harmonic matching pursuit algorithm are achieved.

Son Lam Phung - One of the best experts on this subject based on the ideXlab platform.

  • sparse representation of gpr traces with application to signal classification
    IEEE Transactions on Geoscience and Remote Sensing, 2013
    Co-Authors: Wenbin Shao, Abdesselam Bouzerdoum, Son Lam Phung
    Abstract:

    Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete Dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor Dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation and yields discriminative features for pattern classification.