Unequal Error Protection for S3AC Coding Based on Expanding Window Fountain Codes

Cited 2|Views19

Abstract

This paper presents a coding scheme to improve the performance of three-dimensional (3D) audio. The scheme is designed by the idea of joint source channel coding (JSCC) and is implemented by Expanding Window Fountain (EWF) codes for the 3D audio bitstreams after source coding of spatial squeeze surround audio coding (S3AC). EWF is one of unequal error protection (UEP) LT codes and the proposed scheme is achieved by that method for the case of two levels. Different from other transmissions with equal error protection (EEP) for each part of bitstreams, when transmitting the two parts of the bitstreams with downmixed mono signals and spatial side information after S3AC coding, this approach provides more protection to the part of spatial side information and comparatively less protection to the downmixed mono signals, and results in the improvement of the performance of the reproduced 3D audio especially on the aspect of the spatial perception. Objective simulation experiment has shown the proposed UEP scheme achieves a better performance than the EEP scheme on the aspect of spatial perception, for the bits error rates (BER) of spatial parameters can decrease dramatically to a low value of about 10-4, but BERs of downmixed mono signals and the case of EEP just decrease slightly to about 10-3.

Translated text

Key words

S(3)AC coding,3D audio,unequal error protection,LT codes,EWF codes

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper