12,228 research outputs found
Sparse Signal Separation in Redundant Dictionaries
We formulate a unified framework for the separation of signals that are
sparse in "morphologically" different redundant dictionaries. This formulation
incorporates the so-called "analysis" and "synthesis" approaches as special
cases and contains novel hybrid setups. We find corresponding coherence-based
recovery guarantees for an l1-norm based separation algorithm. Our results
recover those reported in Studer and Baraniuk, ACHA, submitted, for the
synthesis setting, provide new recovery guarantees for the analysis setting,
and form a basis for comparing performance in the analysis and synthesis
settings. As an aside our findings complement the D-RIP recovery results
reported in Cand\`es et al., ACHA, 2011, for the "analysis" signal recovery
problem: minimize_x ||{\Psi}x||_1 subject to ||y - Ax||_2 \leq {\epsilon}, by
delivering corresponding coherence-based recovery results.Comment: Proc. of IEEE International Symposium on Information Theory (ISIT),
Boston, MA, July 201
Inpainting of long audio segments with similarity graphs
We present a novel method for the compensation of long duration data loss in
audio signals, in particular music. The concealment of such signal defects is
based on a graph that encodes signal structure in terms of time-persistent
spectral similarity. A suitable candidate segment for the substitution of the
lost content is proposed by an intuitive optimization scheme and smoothly
inserted into the gap, i.e. the lost or distorted signal region. Extensive
listening tests show that the proposed algorithm provides highly promising
results when applied to a variety of real-world music signals
Corrupted Sensing with Sub-Gaussian Measurements
This paper studies the problem of accurately recovering a structured signal
from a small number of corrupted sub-Gaussian measurements. We consider three
different procedures to reconstruct signal and corruption when different kinds
of prior knowledge are available. In each case, we provide conditions for
stable signal recovery from structured corruption with added unstructured
noise. The key ingredient in our analysis is an extended matrix deviation
inequality for isotropic sub-Gaussian matrices.Comment: To appear in Proceedings of IEEE International Symposium on
Information Theory 201
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