A synthetic sleep snoring study through the use of linear predictive speech techniques

M. Rezki, M. Ayad
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Abstract

Snoring is a disagreeable sound produced by humans while they sleep and in some dimensions, it is considered pathology. Characterized by inspiratory signals, it is closely related to the breathing function. This paper deals with the sleeping snore using an efficient approach based on the synthesis of a recorded snoring signal. The advantages of this approach are very varied such as offers of a non-contact substitute, artificial reproduction by machine of the original signal (snoring), which can even be integrated later in humanoid robots as an example. The method itself of this reconstitution is a reproduce of the signal through the application of some predictive techniques such as LPC (linear predictive coding), and CELP (Code-excited linear prediction). The difference between original and synthetic signals, called also residuals, can be explained by a scanning factor and different types of noises. Finally, to evaluate our approach, we compute the Segmental Signal to Noise Ratio (called segmental SNR which is a special SNR very useful for segmented signals.), and Root Mean Square Error (RMSE), both of which are suitable criteria for sound signals, decisive for us in order to show the effectiveness of these different methods.
通过使用线性预测语音技术的合成睡眠打鼾研究
打鼾是人类睡觉时发出的一种令人讨厌的声音,在某些方面,它被认为是一种病态。它以吸气信号为特征,与呼吸功能密切相关。本文采用一种基于记录的鼾声信号合成的有效方法来处理睡眠鼾声。这种方法的优点是多种多样的,例如提供非接触式替代品,通过机器人工复制原始信号(打鼾),甚至可以稍后集成到人形机器人中作为一个例子。这种重构方法本身就是通过应用一些预测技术,如LPC(线性预测编码)和CELP(码激励线性预测),对信号进行再现。原始信号和合成信号之间的差异,也称为残差,可以用扫描因子和不同类型的噪声来解释。最后,为了评估我们的方法,我们计算了分段信噪比(称为分段信噪比,这是一种对分段信号非常有用的特殊信噪比)和均方根误差(RMSE),这两者都是适合声音信号的标准,对我们来说是决定性的,以显示这些不同方法的有效性。
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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