基于Emd和压缩感知的语音增强

Wang Dan, Wang Xia, Wang Guangyan, Zhang Yan
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引用次数: 1

摘要

压缩感知作为一种新颖的框架,只需要少量的特征数据就可以对原始数据进行重构,突破了Nyquist采样定律的限制,有效缓解了实际应用中处理大量数据的压力。但是传统压缩感知的降噪性能很差,为了改善这一现状,本文提出了一种将压缩感知技术与经验模式沉积信号分析方法相结合的新系统,该系统兼具压缩感知和经验模式沉积的优点。实验结果表明,该系统不仅能很好地重建语音信号,而且具有良好的降噪效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speech enhancement based on Emd and compressed sensing
Compressed sensing, as a novel framework, needs a small amount of characteristic datas to reconstruct the original datas and breakthroughs the limitation of the Nyquist sampling law, which effectively relieves the pressure of dealing with a large amount of datas in the practical application. But the noise reduction performance in traditional compressed sensing is very poor, in order to improve the situation, this paper proposes a new system that combines compressed sensing techniques with empirical mode deposition signal analysis method, which possesses both the advantages of the compressed sensing and empirical mode deposition. Experimental results show that the system not only can reconstruct the speech signal well, but also plays good noise reduction effect.
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