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Performance measurement in blind audio source separation
TLDR
This paper considers four different sets of allowed distortions in blind audio source separation algorithms, from time-invariant gains to time-varying filters, and derives a global performance measure using an energy ratio, plus a separate performance measure for each error term. Expand
Wavelets on Graphs via Spectral Graph Theory
TLDR
A novel method for constructing wavelet transforms of functions defined on the vertices of an arbitrary finite weighted graph using the spectral decomposition of the discrete graph Laplacian L, based on defining scaling using the graph analogue of the Fourier domain. Expand
Under-Determined Reverberant Audio Source Separation Using a Full-Rank Spatial Covariance Model
This paper addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixtureExpand
The Cosparse Analysis Model and Algorithms
TLDR
This work proposes effective pursuit methods that aim to solve inverse problems regularized with the analysis-model prior, accompanied by a preliminary theoretical study of their performance. Expand
Sparse representations in unions of bases
TLDR
It is proved that the result of Donoho and Huo, concerning the replacement of the /spl lscr//sup 0/ optimization problem with a linear programming problem when searching for sparse representations has an analog for dictionaries that may be highly redundant. Expand
Mptk: Matching Pursuit Made Tractable
TLDR
A new architecture is proposed which exploits the structure shared by many redundant MP dictionaries, and thus decreases its complexity to O(N log N), which makes it possible, from now on, to explore and apply MP in the framework of real-life, high-dimensional data processing problems. Expand
Random sampling of bandlimited signals on graphs
TLDR
Signal processing on graphs on graphs and machine learning using EPFL-TALK-214904 as a model for graph supervised learning. Expand
BSS_EVAL Toolbox User Guide -- Revision 2.0
TLDR
The purpose of this toolbox is to measure the performance of various source separation algorithms in an evaluation framework where the original sources, and perhaps even the noise that perturbed the mixture, are available for comparison. Expand
A survey of Sparse Component Analysis for blind source separation: principles, perspectives, and new challenges
TLDR
This survey highlights the appealing features and challenges of Sparse Component Analysis for blind source separation (BSS) and discusses how SCA could be used to exploit both the spatial diversity corresponding to the mixing process and the morphological diversity between sources to unmix even underdetermined convolutive mixtures. Expand
Audio Inpainting
TLDR
The audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss is proposed and this approach is shown to outperform state-of-the-art and commercially available methods for audio declipping in terms of Signal-to-Noise Ratio. Expand
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