Efficient algorithms for burst error recovery using FFT and other transform kernels

In this paper, we will show that the problem of signal reconstruction from missing samples can be handled by using reconstruction algorithms similar t o the Reed-Solomon (RS) decoding techniques. Usually, the RS algorithm is u sed for error detection and correction of samples in finite fields, For the case of missing samples of a speech signal, we work with samples in the fi eld of real or complex numbers, and we can use FFT or some new transforms i n the reconstruction algorithm. DSP implementation and simulation results s how that the proposed methods are better than the ones previously published in terms of the quality of recovered speech signal for a given complexity, The burst error recovery method using the FFT kernel is sensitive to quant ization and additive noise like the other techniques. However, other propos ed transform kernels are very robust in correcting bursts of errors with th e presence of quantization and additive noise.