Consistent Reconstruction of the Input of an Oversampled Filter Bank From Noisy Subbands

TL;DR

This paper presents a maximum-likelihood reconstruction method for oversampled filter bank inputs from noisy, quantized subbands, leveraging redundancy and bounded quantization noise to improve signal recovery.

Contribution

It introduces a novel reconstruction approach that exploits OFB redundancy and bounded quantization noise, providing significant performance improvements over classical decoders.

Findings

Up to 9 dB SNR gain in reconstructed signals

3 dB channel SNR improvement

Effective reconstruction in noisy, quantized environments

Abstract

This paper introduces a reconstruction approach for the input signal of an oversampled filter bank (OFB) when the sub-bands generated at its output are quantized and transmitted over a noisy channel. This approach exploits the redundancy introduced by the OFB and the fact that the quantization noise is bounded. A maximum-likelihood estimate of the input signal is evaluated, which only considers the vectors of quantization indexes corresponding to subband signals that could have been generated by the OFB and that are compliant with the quantization errors. When considering an OFB with an oversampling ratio of 3/2 and a transmission of quantized subbands on an AWGN channel, compared to a classical decoder, the performance gains are up to 9 dB in terms of SNR for the reconstructed signal, and 3 dB in terms of channel SNR.