Python implementation of a Matching Pursuit algorithm discusses in Matching Pursuit with Time-Frequency Dictionary, S. G. Mallat, Z. Zhang, IEEE transaction 1993, for sound processing.
- Import
from utils.matching_sound_processing import MSP- Define MSP object, specifying target path, source path and sample rate.
mp = MSP(target_path=TARGET, source_path=SOURCE, sr=SR)- Create atoms and time-frequency dictionary, specifying:
- decomposition mode: str, fixed or variable.
- The argumets associated: if fixed, you will have to define wlen (win length in samples) and hopsize (in percent = hopsize * wlen); if varbiale, you will to define wlenmin, wlenmax, hopsizemin, hopsizemax and number of lengths to generate, nwin
mp.generate_atoms(mode="variable", wlenmin=1024, wlenmax=4096, hopsizemin=0.25, hopsizemax=3, nwin=10)- Generate matching atoms, specifying: the max number of atoms to be extract k and the max error eps
mp.matching(k=10, eps=1e-6)- rebuild target signal (return 1D array)
y = mp.perform_rebuild()It is possibile access to all parameters:
# target frames
target_atoms = mp.matching_pursuit.target_atoms
# dictionary
dictionary = mp.matching_pursuit.dictionary
# coeffs with best index
coeffs = mp.matching_pursuit.coeffs
# atoms with best index
atoms = mp.matching_pursuit.atoms
# result of the product between coeffs and atoms with best index (2D)
matching_atoms = mp.matching_pursuit.matching_atoms...and plotting the results
mp.plot_results()