Making sense of randomness: Fast signal recovery from compressive samples. Abrol, V., Sharma, P., & Sao, A. K. In 2016 24th European Signal Processing Conference (EUSIPCO), pages 56-60, Aug, 2016.
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Paper doi abstract bibtex
Paper doi abstract bibtex In compressed sensing (CS) framework, a signal is sampled below Nyquist rate, and the acquired samples are generally random in nature. Thus, for efficient estimation of the actual signal, the sensing matrix must preserve the relative distances among the underlying sparse vectors. Provided this condition is fulfilled, we show that CS samples will also preserve the envelope of the actual signal. Exploiting this envelope preserving property of CS samples, we propose a new fast method which is able to extract prototype signals from compressive samples for efficient sparse representation and recovery of signals. These prototype signals are orthogonal intrinsic mode functions (IMFs) extracted from CS samples using empirical mode decomposition (EMD), which is one of the popular methods to capture the envelope of a signal. The extracted IMFs are used to seed the dictionary without even comprehending the original signal or the sensing matrix. Moreover, one can update the dictionary on-line as new CS samples are available. In particularly, to recover first L signals (ϵ Rn) at the decoder, one can seed the dictionary in just O(nL log n) operations, that is far less as compared to existing approaches. The efficiency of the proposed approach is demonstrated experimentally for recovery of speech signals.
@InProceedings{7760209,
author = {V. Abrol and P. Sharma and A. K. Sao},
booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
title = {Making sense of randomness: Fast signal recovery from compressive samples},
year = {2016},
pages = {56-60},
abstract = {In compressed sensing (CS) framework, a signal is sampled below Nyquist rate, and the acquired samples are generally random in nature. Thus, for efficient estimation of the actual signal, the sensing matrix must preserve the relative distances among the underlying sparse vectors. Provided this condition is fulfilled, we show that CS samples will also preserve the envelope of the actual signal. Exploiting this envelope preserving property of CS samples, we propose a new fast method which is able to extract prototype signals from compressive samples for efficient sparse representation and recovery of signals. These prototype signals are orthogonal intrinsic mode functions (IMFs) extracted from CS samples using empirical mode decomposition (EMD), which is one of the popular methods to capture the envelope of a signal. The extracted IMFs are used to seed the dictionary without even comprehending the original signal or the sensing matrix. Moreover, one can update the dictionary on-line as new CS samples are available. In particularly, to recover first L signals (ϵ Rn) at the decoder, one can seed the dictionary in just O(nL log n) operations, that is far less as compared to existing approaches. The efficiency of the proposed approach is demonstrated experimentally for recovery of speech signals.},
keywords = {compressed sensing;signal reconstruction;signal representation;speech processing;vectors;speech signal recovery;EMD;empirical mode decomposition;orthogonal IMF;orthogonal intrinsic mode functions;sparse representation;signal envelope preservation;sparse vectors;sensing matrix;signal estimation;Nyquist rate;CS framework;compressed sensing;fast signal recovery;Dictionaries;Speech;Sensors;Sparse matrices;Image coding;Prototypes;Buildings;Compressed sensing;dictionary learning;empirical mode decomposition;speech processing},
doi = {10.1109/EUSIPCO.2016.7760209},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570250527.pdf},
}Downloads: 0
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Sao},\n booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},\n title = {Making sense of randomness: Fast signal recovery from compressive samples},\n year = {2016},\n pages = {56-60},\n abstract = {In compressed sensing (CS) framework, a signal is sampled below Nyquist rate, and the acquired samples are generally random in nature. Thus, for efficient estimation of the actual signal, the sensing matrix must preserve the relative distances among the underlying sparse vectors. Provided this condition is fulfilled, we show that CS samples will also preserve the envelope of the actual signal. Exploiting this envelope preserving property of CS samples, we propose a new fast method which is able to extract prototype signals from compressive samples for efficient sparse representation and recovery of signals. 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