Fault tolerant transform domain adaptive noise Canceling from Corrupted Speech Signals

Abstract

Fault Tolerant Adaptive Filters (FTAFs) rely on inherent learning capabilities of the adaptive process to compensate for transient (soft) or permanent (hard) errors in hardware implementations. This paper investigates fault tolerant transform domain adaptive noise canceling filters to cancel noise from corrupted speech signals. Two transform domain adaptive FIR architectures are compared, one based on the conventional FFT and one on the Modified Discrete Fourier Transform (MDFT), both without zero padding. Results support the fact that the MDFT- based FTAF architecture is able to overcome certain fault conditions that cannot be properly handled with a conventional FFT-based FTAF architecture.