General design algorithm for sparse frame expansions
Signal Processing
Conditional spectral moments in matching pursuit based on the chirplet elementary function
Digital Signal Processing
Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Environmental sound recognition with time-frequency audio features
IEEE Transactions on Audio, Speech, and Language Processing
Parametric dictionary design for sparse coding
IEEE Transactions on Signal Processing
Efficient coding scheme of displaced frame difference
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
A novel signal-based approach to anomaly detection in IDS systems
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
EURASIP Journal on Advances in Signal Processing - Special issue on time-frequency analysis and its applications to multimedia signals
EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Auditory-inspired sparse representation of audio signals
Speech Communication
Matching Pursuits with random sequential subdictionaries
Signal Processing
Hi-index | 35.69 |
We introduce a modified matching pursuit algorithm, called fast ridge pursuit, to approximate N-dimensional signals with M Gaussian chirps at a computational cost O(MN) instead of the expected O(MN2 logN). At each iteration of the pursuit, the best Gabor atom is first selected, and then, its scale and chirp rate are locally optimized so as to get a “good” chirp atom, i.e., one for which the correlation with the residual is locally maximized. A ridge theorem of the Gaussian chirp dictionary is proved, from which an estimate of the locally optimal scale and chirp is built. The procedure is restricted to a sub-dictionary of local maxima of the Gaussian Gabor dictionary to accelerate the pursuit further. The efficiency and speed of the method is demonstrated on a sound signal