Set-membership theory applied to linear prediction analysis of speech

Abstract

The theory of set membership (SM) identification is formulated, and applied to linear prediction (LP) analysis of speech. The LP parameters of a simulated vowel are identified as an illustration. The SM strategy results in a significant computational savings due to rejection of data which are informationless in the SM sense.