In this brief we present a framework for experimenting with lapped linear transforms in modern numerical computation libraries such as NumPy and Julia. We make use of the fact that these transforms can be represented as matrices (and oftentimes as sparse factorizations thereof), and that numerical computation libraries often support strided memory views. This strided memory view very elegantly solves the problem of processing several overlapping frames at once, while simultaneously allowing vectorization.
Authors:
Werner, Nils; Edler, Bernd
Affiliation:
International Audio Laboratories Erlangen, Erlangen, Germany
AES Convention:
146 (March 2019)
eBrief:523
Publication Date:
March 10, 2019
Subject:
Production and Simulation
Click to purchase paper as a non-member or you can login as an AES member to see more options.
No AES members have commented on this paper yet.
To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.