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.
(See document for exact affiliation information.)
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.
