Sound masking using Genetic Algorithm & Artificial Neural Network (SMUGAANN)

Francisco B. Culibrina, E. Dadios
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Abstract

One of the significant factor of privacy is the source of sound. It creates the level that determines the effectiveness of the other factor. To experience maximum privacy, cancelling of unwanted sound is necessary. To automate the design of sound masking, this proposal use Genetic Algorithm and Artificial Neural Network (SMUGAAN). By evolutionary method two stages are use: a) to determine the target sound being mask evaluation of the parameters of functional elements and b) analysis of the target sound to get the fitness value to be mask and test signals with the help of Sound Synthesis Algorithm (SSA) technique. This stage gives audible sound to mask the target sound.
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基于遗传算法和人工神经网络的声音掩蔽
隐私的一个重要因素是声音的来源。它创造了决定其他因素有效性的水平。为了获得最大的隐私,消除不必要的声音是必要的。为了实现掩声设计的自动化,本方案采用遗传算法和人工神经网络(SMUGAAN)。该方法分为两个阶段:a)确定目标声音的掩模,对功能元素参数进行评价;b)对目标声音进行分析,利用声音合成算法(sound Synthesis Algorithm, SSA)技术得到目标声音的掩模适应度值并对信号进行测试。这个阶段发出可听到的声音来掩盖目标声音。
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