Pitch-shifting audio signals using sparse atomic approximations
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
The influence of polyphony on the dynamical modelling of musical timbre
Pattern Recognition Letters
Transients Detection in the Time-Scale Domain
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Time-scale atoms chains for transients detection in audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Adaptive signal modeling based on sparse approximations for scalable parametric audio coding
IEEE Transactions on Audio, Speech, and Language Processing
Sparse approximation and the pursuit of meaningful signal models with interference adaptation
IEEE Transactions on Audio, Speech, and Language Processing
Music scene-adaptive harmonic dictionary for unsupervised note-event detection
IEEE Transactions on Audio, Speech, and Language Processing
Pattern Recognition Letters
A watermarking-based method for informed source separation of audio signals with a single sensor
IEEE Transactions on Audio, Speech, and Language Processing
A review on techniques for the extraction of transients in musical signals
CMMR'05 Proceedings of the Third international conference on Computer Music Modeling and Retrieval
Matching Pursuits with random sequential subdictionaries
Signal Processing
Original Articles: Time-scale energy based analysis of contours of real-world shapes
Mathematics and Computers in Simulation
| Hi-index | 0.00 |
This paper describes the Molecular Matching Pursuit (MMP), an extension of the popular Matching Pursuit (MP) algorithm for the decomposition of signals. The MMP is a practical solution which introduces the notion of structures within the framework of sparse overcomplete representations; these structures are based on the local dependency of significant time-frequency or time-scale atoms. We show that this algorithm is well adapted to the representation of real signals such as percussive audio signals. This is at the cost of a slight sub-optimality in terms of the rate of convergence for the approximation error, but the benefits are numerous, most notably a significant reduction in the computational cost, which facilitates the processing of long signals. Results show that this algorithm is very promising for high-quality adaptive coding of audio signals