Journal ArticleDOI
Sound source localization using compressive sensing-based feature extraction and spatial sparsity
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TLDR
A source localization algorithm based on a sparse Fast Fourier Transform-based feature extraction method and spatial sparsity which leads to a sparse representation of audio signals and a significant reduction in the dimensionality of the signals.About:
This article is published in Digital Signal Processing.The article was published on 2013-07-01. It has received 15 citations till now. The article focuses on the topics: Feature extraction & Sparse approximation.read more
Citations
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Proceedings ArticleDOI
Model-based sparse component analysis for reverberant speech localization
TL;DR: The results demonstrate the effectiveness of block sparse Bayesian learning framework incorporating autoregressive correlations to achieve a highly accurate localization performance.
Journal ArticleDOI
2-D DOA and mutual coupling coefficient estimation for arbitrary array structures with single and multiple snapshots
Ahmet M. Elbir,T. Engin Tuncer +1 more
TL;DR: In this paper, the joint-sparsity of the array model is exploited to estimate both DOA and MC coefficients with a single snapshot for an unstructured array where the antennas are placed arbitrarily in space.
Journal ArticleDOI
Localization of multiple disjoint sources with prior knowledge on source locations in the presence of sensor location errors
TL;DR: The problem of localizing multiple disjoint sources where prior knowledge on the source locations is available to mitigate the effect of sensor location uncertainty is considered and the Cramer-Rao lower bound (CRLB) is derived.
Journal ArticleDOI
Compressive-Sampling-Based Positioning in Wireless Body Area Networks
TL;DR: A new modeling and analysis framework for the multipatient positioning in a wireless body area network (WBAN) which exploits the spatial sparsity of patients and a sparse fast Fourier transform (FFT)-based feature extraction mechanism for monitoring of Patients and for reporting the movement tracking to a central database server containing patient vital information is presented.
Journal ArticleDOI
Face recognition using a new compressive sensing-based feature extraction method
Mehdi Banitalebi-Dehkordi,Amin Banitalebi-Dehkordi,Jamshid Abouei,Konstantinos N. Plataniotis +3 more
TL;DR: The experiment results show that the combined Compressive Sensing and Sparse Representation Classification (SRC) achieves a high recognition accuracy, while maintaining a reasonable computational complexity.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
An Introduction To Compressive Sampling
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Journal ArticleDOI
Content-based classification, search, and retrieval of audio
TL;DR: The audio analysis, search, and classification engine described here reduces sounds to perceptual and acoustical features, which lets users search or retrieve sounds by any one feature or a combination of them, by specifying previously learned classes based on these features.
Journal ArticleDOI
Signal Processing With Compressive Measurements
TL;DR: This paper takes some first steps in the direction of solving inference problems-such as detection, classification, or estimation-and filtering problems using only compressive measurements and without ever reconstructing the signals involved.
Proceedings Article
Distributed target localization via spatial sparsity
TL;DR: It is shown that the proposed approximation framework can successfully determine multiple target locations by using linear dimensionality-reducing projections of sensor measurements, ameliorating the communication requirements.
Related Papers (5)
Passive Source Localization Using Compressive Sensing.
Recovery of Periodic Clustered Sparse signals from compressive measurements
Chia Wei Lim,Michael B. Wakin +1 more