26,320 research outputs found
Phaseless super-resolution in the continuous domain
Phaseless super-resolution refers to the problem of superresolving a signal
from only its low-frequency Fourier magnitude measurements. In this paper, we
consider the phaseless super-resolution problem of recovering a sum of sparse
Dirac delta functions which can be located anywhere in the continuous
time-domain. For such signals in the continuous domain, we propose a novel
Semidefinite Programming (SDP) based signal recovery method to achieve the
phaseless superresolution. This work extends the recent work of Jaganathan et
al. [1], which considered phaseless super-resolution for discrete signals on
the grid
A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design
Audio fingerprinting, also named as audio hashing, has been well-known as a
powerful technique to perform audio identification and synchronization. It
basically involves two major steps: fingerprint (voice pattern) design and
matching search. While the first step concerns the derivation of a robust and
compact audio signature, the second step usually requires knowledge about
database and quick-search algorithms. Though this technique offers a wide range
of real-world applications, to the best of the authors' knowledge, a
comprehensive survey of existing algorithms appeared more than eight years ago.
Thus, in this paper, we present a more up-to-date review and, for emphasizing
on the audio signal processing aspect, we focus our state-of-the-art survey on
the fingerprint design step for which various audio features and their
tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh
International Conferences on Pervasive Patterns and Applications (PATTERNS
2015), Mar 2015, Nice, Franc
Singular value demodulation of phase-shifted holograms
We report on phase-shifted holographic interferogram demodulation by singular
value decomposition. Numerical processing of optically-acquired interferograms
over several modulation periods was performed in two steps : 1- rendering of
off-axis complex-valued holograms by Fresnel transformation of the
interferograms; 2- eigenvalue spectrum assessment of the lag-covariance matrix
of hologram pixels. Experimental results in low-light recording conditions were
compared with demodulation by Fourier analysis, in the presence of random phase
drifts.Comment: 4 pages, 3 figure
Compressive Phase Retrieval From Squared Output Measurements Via Semidefinite Programming
Given a linear system in a real or complex domain, linear regression aims to
recover the model parameters from a set of observations. Recent studies in
compressive sensing have successfully shown that under certain conditions, a
linear program, namely, l1-minimization, guarantees recovery of sparse
parameter signals even when the system is underdetermined. In this paper, we
consider a more challenging problem: when the phase of the output measurements
from a linear system is omitted. Using a lifting technique, we show that even
though the phase information is missing, the sparse signal can be recovered
exactly by solving a simple semidefinite program when the sampling rate is
sufficiently high, albeit the exact solutions to both sparse signal recovery
and phase retrieval are combinatorial. The results extend the type of
applications that compressive sensing can be applied to those where only output
magnitudes can be observed. We demonstrate the accuracy of the algorithms
through theoretical analysis, extensive simulations and a practical experiment.Comment: Parts of the derivations have submitted to the 16th IFAC Symposium on
System Identification, SYSID 2012, and parts to the 51st IEEE Conference on
Decision and Control, CDC 201
Weak signal identification with semantic web mining
We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time
Score-Informed Source Separation for Musical Audio Recordings [An overview]
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