Name
Abstract | ||
|---|---|---|
Consider the relationship between reconstruction error and sparseness degree and define comparable measures with more detailed information.Define measures at different angles, allowing for evaluation in different tasks. Our measures cover the representation, reconstruction, denoising and separation of speech.Put forward the optimization problem of a given dictionary and solve this problem by removing unimportant atoms and harmful atoms. Recently, sparse coding has attracted considerable attention in speech processing. As a promising technique, sparse coding can be widely used for analysis, representation, compression, denoising and separation of speech. To represent signals accurately and sparsely, a good dictionary which contains elemental signals is preferred and many methods have been proposed to learn such a dictionary. However, there is a lack of reasonable evaluation methods to judge whether a dictionary is good enough. To solve this problem, we define a group of measures for dictionary evaluation. These measures not only address sparseness and reconstruction error of signal representation, but also consider denoising and separating performance. We show how to evaluate dictionaries with these measures, and further propose two methods to optimize dictionaries by improving relative measures. The first method improves the efficiency of sparse coding by removing unimportant atoms; the second one improves denoising performance of dictionaries by removing harmful atoms. Experimental results show that the measures can provide reasonable evaluations and the proposed methods for optimization can further improve given dictionaries. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.ins.2015.03.010 | Inf. Sci. |
Keywords | Field | DocType |
dictionary optimization,speech recognition,dictionary evaluation,sparse coding,speech denoising | Noise reduction,Speech processing,Speech denoising,K-SVD,Pattern recognition,Computer science,Neural coding,Reconstruction error,Artificial intelligence,Optimization problem,Machine learning | Journal |
Volume | Issue | ISSN |
310 | C | 0020-0255 |
Citations | PageRank | References |
3 | 0.37 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y He | 1 | 12 | 6.46 |
| Deyun Chen | 2 | 21 | 10.35 |
| Guang-Lu Sun | 3 | 58 | 16.03 |
| jiqing han | 4 | 105 | 26.46 |