基于压缩感知和稀疏快速傅里叶变换(SFFT)的音频压缩的比较研究:性能和挑战

Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology Pub Date : 2013-12-01 DOI:10.1109/ISSPIT.2013.6781923

Hossam M. Kasem, M. Elsabrouty

{"title":"基于压缩感知和稀疏快速傅里叶变换(SFFT)的音频压缩的比较研究:性能和挑战","authors":"Hossam M. Kasem, M. Elsabrouty","doi":"10.1109/ISSPIT.2013.6781923","DOIUrl":null,"url":null,"abstract":"Audio compression has become one of the basic multimedia technologies. Choosing an efficient compression scheme that is capable of preserving the signal quality while providing a high compression ratio is desirable in the different standards worldwide. In this paper we study the application of two highly acclaimed sparse signal processing algorithms, namely, Compressed Sensing (CS) and Sparse Fart Fourier transform, to audio compression. In addition, we present a Sparse Fast Fourier transform (SFFT)-based framework to compress audio signal. This scheme embeds the K-largest frequencies indices as part of the transmitted signal and thus saves in the bandwidth required for transmission.","PeriodicalId":88960,"journal":{"name":"Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology","volume":"178 1","pages":"000452-000457"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A comparative study of audio compression based on compressed sensing and Sparse Fast Fourier transform (SFFT): Performance and challenges\",\"authors\":\"Hossam M. Kasem, M. Elsabrouty\",\"doi\":\"10.1109/ISSPIT.2013.6781923\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Audio compression has become one of the basic multimedia technologies. Choosing an efficient compression scheme that is capable of preserving the signal quality while providing a high compression ratio is desirable in the different standards worldwide. In this paper we study the application of two highly acclaimed sparse signal processing algorithms, namely, Compressed Sensing (CS) and Sparse Fart Fourier transform, to audio compression. In addition, we present a Sparse Fast Fourier transform (SFFT)-based framework to compress audio signal. This scheme embeds the K-largest frequencies indices as part of the transmitted signal and thus saves in the bandwidth required for transmission.\",\"PeriodicalId\":88960,\"journal\":{\"name\":\"Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology\",\"volume\":\"178 1\",\"pages\":\"000452-000457\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPIT.2013.6781923\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT.2013.6781923","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 10

摘要

音频压缩已成为多媒体技术的基础之一。选择一种既能保持信号质量又能提供高压缩比的有效压缩方案是世界范围内不同标准所需要的。本文研究了两种广受好评的稀疏信号处理算法，即压缩感知(CS)和稀疏傅立叶变换在音频压缩中的应用。此外，我们提出了一种基于稀疏快速傅立叶变换(SFFT)的音频信号压缩框架。该方案将k最大的频率指标嵌入到传输信号中，从而节省了传输所需的带宽。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A comparative study of audio compression based on compressed sensing and Sparse Fast Fourier transform (SFFT): Performance and challenges

Audio compression has become one of the basic multimedia technologies. Choosing an efficient compression scheme that is capable of preserving the signal quality while providing a high compression ratio is desirable in the different standards worldwide. In this paper we study the application of two highly acclaimed sparse signal processing algorithms, namely, Compressed Sensing (CS) and Sparse Fart Fourier transform, to audio compression. In addition, we present a Sparse Fast Fourier transform (SFFT)-based framework to compress audio signal. This scheme embeds the K-largest frequencies indices as part of the transmitted signal and thus saves in the bandwidth required for transmission.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology

自引率

0.00%

发文量