Name
Title | ||
|---|---|---|
Lossy audio signal compression via structured sparse decomposition and compressed sensing |
Abstract | ||
|---|---|---|
In this paper, we propose a method for lossy audio signal compression via structured sparse decomposition and compressed sensing (CS). In this method, a least absolute shrinkage and selection operator (LASSO) is employed to sparse and structured decompose the audio signals into tonal and transient layers, and then, both resulting layers are compressed by a CS method. By employing a new penalty term, which takes advantage of the structure information of transform coefficients, the LASSO is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two resulting layers, thus improving the performance of CS. Experimental results showed that the new method provided a better compression performance than conventional methods did. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICME.2014.6890235 | ICME |
Keywords | Field | DocType |
audio coding,audio compression,approximation theory,compressed sensing,least absolute shrinkage and selection operator,data compression,sparse approximation,penalty term,lasso,sparsity allocation algorithm,lossy audio signal compression,structured sparse decomposition,dictionaries,time frequency analysis,estimation,signal to noise ratio | Audio signal,Data compression ratio,Pattern recognition,Lossy compression,Computer science,Lasso (statistics),Sparse approximation,Artificial intelligence,Data compression,Dynamic range compression,Compressed sensing | Conference |
ISSN | Citations | PageRank |
1945-7871 | 0 | 0.34 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sumxin Jiang | 1 | 7 | 2.87 |
| Rendong Ying | 2 | 75 | 19.11 |
| Zhenqi Lu | 3 | 11 | 2.02 |
| Pei-Lin Liu | 4 | 231 | 44.49 |
| Zenghui Zhang | 5 | 50 | 10.29 |