Application of 1-D discrete wavelet transform based compressed sensing matrices for speech compression

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Table 23 Performance analysis of the proposed Beylkin wavelet based sensing matrix

Length of signal (N)	Number of measurements (m)	Compression ratio (CR = m/N)	Sparsity level = (k/N) × 100 (%)	No. of non-zeros (k)	No. of iterations required	Signal reconstruction time (s)	RMSE	Relative error	SNR (db)	Construction time for sensing matrix (s)
2048	205	0.1	50	1024	17	1.351917	0.0551	0.9205	0.7196	2.635292
2048	410	0.2	50	1024	16	2.313493	0.0522	0.8726	1.1837	2.500319
2048	512	0.25	50	1024	16	3.655763	0.0522	0.8722	1.1878	2.582726
2048	614	0.3	50	1024	16	4.267978	0.0490	0.8193	1.7316	2.668384
2048	849	0.4	50	1024	14	5.865898	0.0483	0.8064	1.8686	2.501577
2048	1024	0.5	50	1024	14	9.541513	0.0482	0.8056	1.8774	2.626348
2048	1229	0.6	50	1024	13	11.113448	0.0469	0.7838	2.1163	2.464044
2048	1434	0.7	50	1024	13	14.567693	0.0468	0.7822	2.1341	2.504371
2048	1536	0.75	50	1024	12	17.19308	0.0468	0.7816	2.1405	2.527696
2048	1638	0.8	50	1024	12	18.081755	0.0468	0.7816	2.1406	2.516479
2048	1843	0.9	50	1024	12	22.526654	0.0468	0.7816	2.1407	2.523531
2048	2048	1.0	50	1024	9	22.950703	0.0468	0.7815	2.1409	2.466272