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

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