自适应人工神经网络的FPGA实现及其在说话人识别中的应用

F. A. Elmisery, A. Khalil, A. Salama, F. Algeldawy
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引用次数: 1

摘要

说话人识别是一项具有挑战性的模式分类任务。它被广泛应用于安全系统、信息检索服务等许多应用中,预计便携式识别系统将在未来的许多用途中得到广泛应用,例如移动应用。使用专用硬件实现识别技术对于实现智能单元非常有用。在这种情况下,FPGA可以为实现模式分类策略提供一种有效的技术。说话人识别系统可以使用多种分类方法来实现,其中人工神经网络(ANN)被认为是最强大的分类技术之一。神经网络的实现。由于所需的算术运算的复杂性,FPGA是一项具有挑战性的任务。本文对非线性激活函数进行了调整,使其更适合于FPGA实现。我们使用多层感知器神经网络(MLP NN)达到了几乎100%的识别率。
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Adaptation of ann for FPGA implementation and its application for speaker identification
Speaker identification is a challenging pattern classification task. It is used enormously in many applications such as security systems, information retrieved services, etc. portable identification systems are expected to be widely used in future in many purposes, such as mobile applications. Implementing the identification technique using a dedicated hardware could be very useful to achieve smart units. In this context, the FPGA could offer an efficient technology to realize a pattern classification strategy. A speaker identification system can be implemented using many classification approaches, one of these , the artificial neural network (ANN), which is considered one of the most powerful classification techniques. Implementing a Neural Network on. an FPGA is a challenging task because of the complexity of the required arithmetic operations. In this paper the nonlinear activation function is adapted to be more suitable for the FPGA implementation. We have reached almost 100% identification rate using Multi Layer Perceptron Neural Network ( MLP NN).
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