L. Daudet, Audio sparse decompositions in paral- 34 lel, Let the greed be shared!, IEEE Transactions on Signal, vol.35, issue.27, pp.90-96, 2010.

L. Daudet, S. Molla, and B. Torrésani, Towards 37 a hybrid audio coder, Proceedings of the International 38, 2004.

L. Daudet and B. Torrésani, Hybrid representations 41 for audiophonic signal encoding, Signal Processing Journal, vol.42, pp.82-1595, 2002.

L. Daudet and B. Torrésani, Signal Processing Meth- 44 ods for Music Transcription, chapter Sparse Adaptive Rep- 45 resentations for Musical Signals, pp.65-98, 2006.

M. Davies and L. Daudet, Sparse audio representations using the MCLT, Signal Processing, vol.86, issue.3, pp.457-505, 2006.
DOI : 10.1016/j.sigpro.2005.05.024

URL : https://hal.archives-ouvertes.fr/hal-00104908

M. Davies and M. Plumbley, Context-dependent 50 beat tracking of musical audio, IEEE Transactions on Audio, p.51, 2007.

S. Dixon, Evaluation of the Audio Beat Tracking System BeatRoot, Journal of New Music Research, vol.24, issue.1, pp.39-51, 2007.
DOI : 10.1076/jnmr.31.1.37.8103

V. Emiya, E. Vincent, N. Harlander, and H. , Subjective and objective quality assessment of audio 56 source separation, pp.55-57, 2010.

C. Févotte, L. Daudet, S. Godsill, and B. Torresani, Sparse regression with structured priors: Applica- 59 tion to audio denoising, Proceedings of the IEEE Interna- 60 tional Conference on Acoustics, Speech, and Signal Process- 61 ings (ICASSP), 2006.

C. Févotte and S. Godsill, A Bayesian approach 63 for blind separation of sparse sources, IEEE Transactions on, p.64, 2006.

C. Févotte and S. Godsill, Sparse linear regression 66 in unions of bases via Bayesian variable selection, IEEE Sig- 67 nal Processing Letters, pp.441-444, 2006.

M. Figueiredo, Adaptive Sparseness for Supervised 73, 2003.

T. Fujishima, Real-time chord recognition of musical 76 sound: a system using common lisp music, Proceedings of 77 the International Computer Music Conference (ICMC), pp.464-78, 1999.

S. Geman and D. Geman, Stochastic relaxation, p.80, 1984.

J. Geweke, Variable Selection and Model Compari- 86 son in Regression, Bayesian Statistics, vol.5, pp.609-620, 1996.

K. Hamdy, M. Ali, and A. Tewfi, Low bit rate high 88 quality audio coding with combined harmonic and wavelet 89 representations, Proceedings of the IEEE International 90, 1996.

C. Harte and M. Sandler, Automatic chord iden- 93 tification using a quantised chromagram, Proceedings of 94 the Convention Audio Engineering Society (AES), p.95, 2005.

F. Jaillet and B. Torrésani, Time-frequency jigsaw 97 puzzle-Adaptive multiwindow and multilayered gabor expan- 98 sions, 2004.

A. Klapuri, A. Eronen, and J. Astola, Analysis of 100 the meter of acoustic musical signals, IEEE Transactions on, vol.101, 2006.

M. Kowalski, Sparse regression using mixed norms, Applied and Computational Harmonic Analysis, vol.27, issue.3, pp.303-324, 2009.
DOI : 10.1016/j.acha.2009.05.006

URL : https://hal.archives-ouvertes.fr/hal-00202904

M. Kowalski and B. Torrésani, Random models for 105 sparse signals expansion on unions of bases with application 106 to audio signals, IEEE Transactions on Signal Processing, vol.107, pp.56-3468, 2008.

F. Low, Complete sets of wave packets A Passion for Physics -Essay in Honor of Geoffrey 115, C. DeTar, vol.114, 1985.

S. Mallat, A Wavelet Tour of Signal Processing, 1998.

H. Malvar, Lapped transforms for efficient trans- 122 form/subband coding, IEEE Transactions on Acoustics, p.123, 1990.

S. Molla and B. Torésani, Determining local, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00350469