2015/10/12 by Miguel Domínguez, Miguel Dominguez, Dominguez, Miguel +4
Computer Science · Engineering · Mathematics · #94 #Analog and Mixed-Signal Circuit Design #Artificial intelligence #Basis (linear algebra) #Basis pursuit #Compressed sensing #Computer science #FOS: Computer and information sciences #Geometry #Information Theory (cs.IT) #Matching pursuit #Mathematics #Microwave Imaging and Scattering Analysis #Sparse and Compressive Sensing Techniques #cs.IT #math.IT #msc:94
paper · pdf · doi:10.48550/arxiv.1510.03709
published in arXiv (Cornell University) (Cornell University) · 5 pages, 3 figures, 1 table, 6 equations, submitted to ICASSP 2016 but not yet accepted or rejected
arxiv created 2015/10/12 · openalex publication_date 2015/10/12 · arxiv updated 2015/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In compressive sensing (CS) theory, as the number of samples is decreased below a minimum threshold, the average error of the recovery increases. Sufficient sampling is either required for quality reconstruction or the error is resignedly accepted. However, most CS work has not taken advantage of the inherent structure in a variety of signals relevant to engineering applications. Hence, this paper proposes a new method of recovery built on basis pursuit (BP), called Structure-Constrained Basis Pursuit (SCBP), that constrains signals based on known structure rather than through extra sampling. Preliminary assessments of this method on TIMIT recordings of the speech phoneme /aa/ show a substantial decrease in error: with a fixed 5:1 compression ratio the average recovery error is 23.8% lower versus vanilla BP. More significantly, this method can be applied to any CS application that samples structured data, such as FSK waveforms, speech, and tones. In these cases, higher compression ratios can be reached with comparable error.