Matching Pursuit Decompositions of Non-Noisy Speech Signals Using Several Dictionaries

Matching pursuit (MP) provides a way to expand signals in terms of any set of time-limited functions, or atoms, called a dictionary. These decompositions are finding use in signal analysis and coding. It has been shown that a dictionary should be designed carefully, but its effects on decomposition have not been studied in detail. We look at the effects of dictionaries on the decomposition of non-noisy speech signals using MP, by five dictionaries. It is found that Gabor atoms work sufficiently well, and have fewer adverse effects in reconstruction compared to the other dictionaries. For a reconstruction to sound perceptually close to the original, a rate of 3000 atoms per second (aps) on average is required. At rates as low as 400 aps the speech remains intelligible. Finally, the use of decompositions to visualize time-frequency distributions of speech is explored