The Experts below are selected from a list of 206637 Experts worldwide ranked by ideXlab platform
Gael Richard - One of the best experts on this subject based on the ideXlab platform.
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Drum extraction in single channel audio signals using multi-layer non negative Matrix Factor deconvolution
2017Co-Authors: Clément Laroche, Hélène Papadopoulos, Matthieu Kowalski, Gael RichardAbstract:In this paper, we propose a supervised multilayer Factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative Matrix Factorization which is able to exploit time-frequency dictionaries to take into account non stationary drum sounds. The drum dictionaries represent various real drum hits and the decomposition has more physical sense and allows for a better interpretation of the results. Experiments on real music data for a harmonic/percussive source separation task show that our method outperforms other state of the art algorithms. Finally, our method is very robust to non stationary harmonic sources that are usually poorly decomposed by existing methods.
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ICASSP - Drum extraction in single channel audio signals using multi-layer Non negative Matrix Factor Deconvolution
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Clément Laroche, Hélène Papadopoulos, Matthieu Kowalski, Gael RichardAbstract:In this paper, we propose a supervised multilayer Factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative Matrix Factorization which is able to exploit time-frequency dictionaries to take into account non stationary drum sounds. The drum dictionaries represent various real drum hits and the decomposition has more physical sense and allows for a better interpretation of the results. Experiments on real music data for a harmonic/percussive source separation task show that our method outperforms other state of the art algorithms. Finally, our method is very robust to non stationary harmonic sources that are usually poorly decomposed by existing methods.
Clément Laroche - One of the best experts on this subject based on the ideXlab platform.
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Drum extraction in single channel audio signals using multi-layer non negative Matrix Factor deconvolution
2017Co-Authors: Clément Laroche, Hélène Papadopoulos, Matthieu Kowalski, Gael RichardAbstract:In this paper, we propose a supervised multilayer Factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative Matrix Factorization which is able to exploit time-frequency dictionaries to take into account non stationary drum sounds. The drum dictionaries represent various real drum hits and the decomposition has more physical sense and allows for a better interpretation of the results. Experiments on real music data for a harmonic/percussive source separation task show that our method outperforms other state of the art algorithms. Finally, our method is very robust to non stationary harmonic sources that are usually poorly decomposed by existing methods.
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ICASSP - Drum extraction in single channel audio signals using multi-layer Non negative Matrix Factor Deconvolution
2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017Co-Authors: Clément Laroche, Hélène Papadopoulos, Matthieu Kowalski, Gael RichardAbstract:In this paper, we propose a supervised multilayer Factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative Matrix Factorization which is able to exploit time-frequency dictionaries to take into account non stationary drum sounds. The drum dictionaries represent various real drum hits and the decomposition has more physical sense and allows for a better interpretation of the results. Experiments on real music data for a harmonic/percussive source separation task show that our method outperforms other state of the art algorithms. Finally, our method is very robust to non stationary harmonic sources that are usually poorly decomposed by existing methods.
Didier Henrion - One of the best experts on this subject based on the ideXlab platform.
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A Toeplitz algorithm for polynomial J-spectral Factorization
Automatica, 2006Co-Authors: Juan Carlos Zúñiga, Didier HenrionAbstract:A block Toeplitz algorithm is proposed to perform the J-spectral Factorization of a para-Hermitian polynomial Matrix. The input Matrix can be singular or indefinite, and it can have zeros along the imaginary axis. The key assumption is that the finite zeros of the input polynomial Matrix are given as input data. The algorithm is based on numerically reliable operations only, namely computation of the null-spaces of related block Toeplitz matrices, polynomial Matrix Factor extraction and linear polynomial Matrix equations solving.
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An algorithm for polynomial Matrix Factor extraction
International Journal of Control, 2000Co-Authors: Didier Henrion, Michael SebekAbstract:An algorithm is described for extracting a polynomial Matrix Factor featuring any subset of the zeros of a given non-singular polynomial Matrix. It is assumed that the zeros to be extracted are given as input data. Complex or repeated zeros are allowed. The algorithm is based on interpolation and relies upon numerically reliable subroutines only. It makes use of a procedure that computes the generalized characteristic vectors of a polynomial Matrix at a given point. The extracted Factor is provided in column- and row-reduced Popov form. Applications of the algorithm include polynomial Matrix interpolation, plus/minus Factorization, column- and row-reduction, or computation of the Smith form of a polynomial Matrix. The numerical routines described in this paper are implemented in the new release 2.0 of the Polynomial Toolbox for MATLAB.
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An algorithm for polynomial Matrix Factor extraction
Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 1Co-Authors: Didier Henrion, Michael SebekAbstract:An algorithm is described for extracting a polynomial Matrix Factor featuring any subset of the zeros of a given non-singular polynomial Matrix. It is assumed that the zeros to be extracted are given as input data. Complex or repeated zeros are allowed. The algorithm is based on interpolation and relies upon numerically reliable subroutines only. It makes use of a procedure that computes the generalized characteristic vectors of a polynomial Matrix at a given point. The extracted Factor is provided in column- and row-reduced Popov form. Applications of the algorithm include polynomial Matrix interpolation, plus/minus Factorization, column- and row-reduction, or computation of the Smith form of a polynomial Matrix. The numerical routines described in the paper are implemented in release 2.0 of the Polynomial Toolbox for MATLAB.
Zhigang Zhao - One of the best experts on this subject based on the ideXlab platform.
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Ultra-high performance liquid chromatography tandem mass spectrometry for cyclosporine analysis in human whole blood and comparison with an antibody-conjugated magnetic immunoassay
Therapeutic Drug Monitoring, 2017Co-Authors: Jiaqing Wang, Xin Hu, Di Chen, Ming Zhao, Li Yang, Zhigang ZhaoAbstract:Various immunoassays have been used for cyclosporine A (CsA) analysis in human whole blood; however, they could not fully satisfy the requirements of criteria for accuracy and specificity in CsA measurement. The liquid chromatography tandem mass spectrometry is a gold method for CsA analysis. The aim of the study was to develop and validate an ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) method for CsA analysis and establish its agreement with an antibody-conjugated magnetic immunoassay (ACMIA) in clinical sample analysis. An UHPLC-MS/MS method for CsA analysis in human whole blood was developed, validated, and applied in 85 samples, which were also tested by ACMIA. The agreement between UHPLC-MS/MS and ACMIA was evaluated by Bland-Altman plot. The calibration range was 5-2000 ng/mL. The inaccuracy and imprecision were -4.60% to 5.56% and less than 8.57%, respectively. The internal standard-normalized recovery and Matrix Factor were 100.4%-110.5% and 93.5%-107.6%, respectively. The measurements of ACMIA and UHPLC-MS/MS were strongly correlated (r > 0.98). Evaluated by Bland-Altman plot, the 95% limit of agreement of the ACMIA:UHPLC-MS/MS ratio was 88.7%-165.6%, and the mean bias of the ratio was 21.1%. A rapid, simple, accurate, and reliable UHPLC-MS/MS method for CsA analysis in human whole blood was developed, validated, and applied in 85 samples. On average, 21.1% overestimation was observed in ACMIA compared with that in the UHPLC-MS/MS. Further and larger studies are required to identify whether this degree of variance could be accepted by clinicians.
Rong Chen - One of the best experts on this subject based on the ideXlab platform.
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Constrained Factor Models for High-Dimensional Matrix-Variate Time Series
Journal of the American Statistical Association, 2019Co-Authors: Elynn Y Chen, Ruey S Tsay, Rong ChenAbstract:High-dimensional Matrix-variate time series data are becoming widely available in many scientific fields, such as economics, biology, and meteorology. To achieve significant dimension reduction while preserving the intrinsic Matrix structure and temporal dynamics in such data, Wang et al. (2017) proposed a Matrix Factor model that is shown to provide effective analysis. In this paper, we establish a general framework for incorporating domain or prior knowledge in the Matrix Factor model through linear constraints. The proposed framework is shown to be useful in achieving parsimonious parameterization, facilitating interpretation of the latent Matrix Factor, and identifying specific Factors of interest. Fully utilizing the prior-knowledge-induced constraints results in more efficient and accurate modeling, inference, dimension reduction as well as a clear and better interpretation of the results. In this paper, constrained, multi-term, and partially constrained Factor models for Matrix-variate time series are developed, with efficient estimation procedures and their asymptotic properties. We show that the convergence rates of the constrained Factor loading matrices are much faster than those of the conventional Matrix Factor analysis under many situations. Simulation studies are carried out to demonstrate the finite-sample performance of the proposed method and its associated asymptotic properties. We illustrate the proposed model with three applications, where the constrained Matrix-Factor models outperform their unconstrained counterparts in the power of variance explanation under the out-of-sample 10-fold cross-validation setting.
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modeling dynamic transport network with Matrix Factor models with an application to international trade flow
arXiv: Econometrics, 2019Co-Authors: Elynn Y Chen, Rong ChenAbstract:International trade research plays an important role to inform trade policy and shed light on wider issues relating to poverty, development, migration, productivity, and economy. With recent advances in information technology, global and regional agencies distribute an enormous amount of internationally comparable trading data among a large number of countries over time, providing a goldmine for empirical analysis of international trade. Meanwhile, an array of new statistical methods are recently developed for dynamic network analysis. However, these advanced methods have not been utilized for analyzing such massive dynamic cross-country trading data. International trade data can be viewed as a dynamic transport network because it emphasizes the amount of goods moving across a network. Most literature on dynamic network analysis concentrates on the connectivity network that focuses on link formation or deformation rather than the transport moving across the network. We take a different perspective from the pervasive node-and-edge level modeling: the dynamic transport network is modeled as a time series of relational matrices. We adopt a Matrix Factor model of \cite{wang2018Factor}, with a specific interpretation for the dynamic transport network. Under the model, the observed surface network is assumed to be driven by a latent dynamic transport network with lower dimensions. The proposed method is able to unveil the latent dynamic structure and achieve the objective of dimension reduction. We applied the proposed framework and methodology to a data set of monthly trading volumes among 24 countries and regions from 1982 to 2015. Our findings shed light on trading hubs, centrality, trends and patterns of international trade and show matching change points to trading policies. The dataset also provides a fertile ground for future research on international trade.