On-the-fly and memory-scalable implementation of Hierarchical Lapped Transforms with minimum memory requirement

摘要

We propose an on-the-fly and memory-scalable implementation of Hierarchical Lapped Transforms (HLT) with minimum memory budget. Both the forward and inverse transforms have the advantages of low memory requirement unrelated to the length of the input signal. Experimental results suggest that the proposed algorithm outperforms the existing algorithms with respect to computation efficiency and memory requirement.