Sparse audio representations using the MCLT

被引:21
作者
Davies, ME
Daudet, L
机构
[1] Univ London, Queen Mary, Dept Elect Engn, DSP & Multimedia Grp, London E1 4NS, England
[2] Univ Paris 06, Lab Acoust Musicale, F-75015 Paris, France
基金
英国工程与自然科学研究理事会;
关键词
lapped transforms; overcomplete dictionaries; sparse coding;
D O I
10.1016/j.sigpro.2005.05.024
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We consider sparse representations of audio based around the modulated complex lapped transform (MCLT) and a generalized iteratively reweighted least squares algorithm which can be interpreted as a variation of expectation maximization. We compare this mildly overcomplete representation to the more traditional modified discrete cosine transform (MDCT) in terms of coding cost and explore the possibility of extending it to a dual-resolution analysis using a pair of MCLT transforms, illustrating its potential application for audio modification. (C) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:457 / 470
页数:14
相关论文
共 24 条
[1]  
[Anonymous], 1999, WAVELET TOUR SIGNAL
[2]   Atomic decomposition by basis pursuit [J].
Chen, SSB ;
Donoho, DL ;
Saunders, MA .
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 1998, 20 (01) :33-61
[3]   Hybrid representations for audiophonic signal encoding [J].
Daudet, L ;
Torrésani, B .
SIGNAL PROCESSING, 2002, 82 (11) :1595-1617
[4]  
DAVIES ME, 2004, P EUSIPCO 04
[5]  
DAVIES ME, 2004, UNPUB GEN IRLS SCHEM
[6]  
DAVIES ME, 2003, P IEEE WORKSH APPL S
[7]   MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM [J].
DEMPSTER, AP ;
LAIRD, NM ;
RUBIN, DB .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1977, 39 (01) :1-38
[8]  
FIGUEIREDO M, 2001, NEURAL INFORMATION P
[9]   Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm [J].
Gorodnitsky, IF ;
Rao, BD .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1997, 45 (03) :600-616
[10]   Learning overcomplete representations [J].
Lewicki, MS ;
Sejnowski, TJ .
NEURAL COMPUTATION, 2000, 12 (02) :337-365