Lossy Speech Compression Via Compressed Sensing-Based Kalman Filtering

We present a new algorithm for lossy speech compression. The new algorithm is based on a simple technique for embedding a compressed sensing mechanism within a conventional Kalman filter. As such, it is capable of constructing compressed representations using significantly less samples than what is usually considered necessary.

By: Avishy Carmi; Dimitri Kanevsky; Bhuvana Ramabhadran

Published in: RC24814 in 2009


This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.


Questions about this service can be mailed to reports@us.ibm.com .