Postfiltering Using Log-Magnitude Spectrum for Speech and Audio Coding
Abstract:
Advanced coding algorithms yield high quality signals with good coding efficiency within their target bit-rate ranges, but their performance suffer outside the target range. At lower bitrates, the degradation in performance is because the decoded signals are sparse, which gives a perceptually muffled and distorted characteristic to the signal. Standard codecs reduce such distortions by applying noise filling and post-filtering methods. In this paper, we propose a post-processing method based on modeling the inherent time-frequency correlation in the log-magnitude spectrum. The goal is to improve the perceptual SNR of the decoded signals and, to reduce the distortions caused by signal sparsity. Objective measures show an average improvement of 1.5 dB for input perceptual SNR in range 4 to 18 dB. The improvement is especially prominent in components which had been quantized to zero.
Cite as: Das, S., Bäckström, T. (2018) Postfiltering Using Log-Magnitude Spectrum for Speech and Audio Coding. Proc. Interspeech 2018, 3543-3547, DOI: 10.21437/Interspeech.2018-1027.
BiBTeX Entry:
@inproceedings{Das2018,
author={Sneha Das and Tom Bäckström},
title={Postfiltering Using Log-Magnitude Spectrum for Speech and Audio Coding},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={3543--3547},
doi={10.21437/Interspeech.2018-1027},
url={http://dx.doi.org/10.21437/Interspeech.2018-1027} }