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Understanding the Biological Underpinning of Auditory Perception for Vowel Sounds Using a Type-2 Fuzzy Neural Network

机译:使用2型模糊神经网络了解元音的听觉感知的生物学基础

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The temporal lobe in the human brain is responsible for low-level audio perception, whereas the pre-frontal lobe takes active role in interpreting the audio information. This paper introduces a novel approach to understand the interrelation between the temporal and the pre-frontal lobes of the brain in interpreting vowel sounds. The inter-relation is ascertained by two approaches. The first approach computes correlation measure between the direct brain signals of the said two lobes. The higher the correlation coefficient, the better is the interrelation between the activated lobes. The second approach aims at developing a feature-level mapping between the temporal and the prefrontal lobe brain activations. The motivation of the second approach lies in examining the uniformity in the learnt neural weights after convergence for the same vowel audio stimulus irrespective of the diurnal variations in the brain signals. Although any traditional mapping functions could be utilized to undertake the temporal to prefrontal mapping, we used a type-2 fuzzy neural network to serve the purpose. Experiments undertaken confirm that the weights of the proposed type-2 fuzzy neural net converges faster than its type-1 counterpart and back-propagation neural network. The faster convergence of weights represent that the proposed type-2 fuzzy neural network captures better audio perceptual ability than the rest. The proposed work is expected to find applications in the early detection of disorder in auditory perceptual-ability, usually referred to as Dyslexia.
机译:人脑的颞叶负责低水平的音频感知,而前额叶在解释音频信息中起积极作用。本文介绍了一种新颖的方法来理解在解释元音时大脑的颞叶和前额叶之间的相互关系。相互关系通过两种方法确定。第一种方法计算所述两个瓣的直接脑信号之间的相关性度量。相关系数越高,激活的波瓣之间的相互关系越好。第二种方法旨在建立颞叶和额叶前脑激活之间的特征级映射。第二种方法的动机在于,对于相同的元音音频刺激,不管大脑信号的昼夜变化如何,在收敛后检查学习的神经权重的均匀性。尽管可以使用任何传统的映射功能来进行时间到前额的映射,但我们还是使用了2型模糊神经网络来达到目的。进行的实验证实,所提出的2类模糊神经网络的权重收敛速度快于1类对应神经网络和反向传播神经网络。权重的更快收敛表明,所提出的2类模糊神经网络比其他类型具有更好的音频感知能力。预期拟议的工作将在早期发现听觉感知能力障碍(通常称为阅读障碍)中找到应用。

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