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

We show that the problem of signal reconstruction from missing samples can be handled by using reconstruction algorithms similar to the Reed-Solomon (RS) decoding techniques. Usually, the RS algorithm is used 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 field of real or complex numbers, and we can use FFT or some new transforms in the reconstruction algorithm. DSP implementation and simulation results show 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 quantization and additive noise like the other techniques. However, other proposed transform kernels are very robust in correcting bursts of errors with the presence of quantization and additive noise