A New Robust Voice Activity Detection method based on Genetic Algorithm

M. Farsinejad, M. Analoui
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引用次数: 5

Abstract

In this paper we introduce an efficient genetic algorithm based voice activity detection (GA-VAD) algorithm. The inputs for GA-VAD are zero-crossing difference and a new feature that is extracted from signal envelope parameter, called MULSE (multiplication of upper and lower signal envelope). The voice activity decision is obtained using a Threshold algorithm with additional decision smoothing. The key advantage of this method is its simple implementation and its low computational complexity and introducing a new simple and efficient feature, MULSE, for solving the VAD problem. The MULSE parameter could be appropriate substitution for energy parameter in VAD problems. The GA-based VAD algorithm (GA-VAD) is evaluated using the Timit database. It is shown that the GA-VAD achieves better performance than G. 729 Annex B at any noise level with a high artificial-to-intelligence ratio.
一种基于遗传算法的鲁棒语音活动检测方法
本文介绍了一种高效的基于遗传算法的语音活动检测算法。GA-VAD的输入是过零差和从信号包络参数中提取的新特征MULSE(上下信号包络的乘法)。语音活动判定采用带有附加判定平滑的阈值算法。该方法的主要优点是实现简单,计算复杂度低,并引入了一个新的简单高效的特征MULSE来解决VAD问题。在VAD问题中,MULSE参数可以代替能量参数。基于ga的VAD算法(GA-VAD)使用Timit数据库进行了评估。结果表明,GA-VAD在任何噪声水平下均优于G. 729附件B,具有较高的人工智能比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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