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首页> 外文会议>2017 Intl Conf on Advanced Control Circuits Systems amp; 2017 Intl Conf on New Paradigms in Electronics amp; Information Technology >From a sparse vector to a sparse symmetric matrix for efficient lossy speech compression
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From a sparse vector to a sparse symmetric matrix for efficient lossy speech compression

机译：从稀疏矢量到稀疏对称矩阵，实现有效的有损语音压缩

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摘要

This paper concentrates on developing efficient and robust backward solution for the forward sparse greedy algorithms and applies it in the field of speech compression. All existing backward solutions are based on constraining more and more weights to zero while re-optimizing the remaining nonzero weights to compensate. Our approach is termed Sparse Vector to Sparse Matrix (SVtSM) algorithm and its idea is to replace the k-sparse weights vector with a k-sparse symmetric matrix. The key result of this paper showed that, the replacement approach proved its effectiveness in achieving significant compression ratios at different SNR levels, and to work efficiently than the backward elimination algorithms.
机译：本文致力于为前向稀疏贪婪算法开发高效，鲁棒的后向解决方案，并将其应用于语音压缩领域。所有现有的后向解决方案都基于将越来越多的权重限制为零，同时重新优化剩余的非零权重以进行补偿。我们的方法被称为稀疏向量到稀疏矩阵（SVtSM）算法，其思想是用k稀疏对称矩阵替换k稀疏权向量。本文的主要结果表明，该替代方法证明了其在不同SNR级别下实现显着压缩率的有效性，并且比后向消除算法有效。

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