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首页> 外文会议>2016 IEEE International Workshop on Acoustic Signal Enhancement >Relative impulse response estimation during doubletalk with an artificial neural network-based step size control
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Relative impulse response estimation during doubletalk with an artificial neural network-based step size control

机译：基于人工神经网络的步长控制在双向通话期间的相对冲激响应估计

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The Normalized Least-Mean Squares (NLMS) algorithm is a widely used method for linear system identification (e.g., for Acoustic Echo Cancellation (AEC), where the acoustic path between loudspeaker and microphone needs to be estimated). As soon as interferers or background noise are active, step size control becomes a crucial task in order to ensure a fast but stable adaptation. Conventional step size control methods address the case of additive noise contaminating the system output, i.e., microphone signal. When the NLMS algorithm is used for Relative Impulse Response (RIR) estimation, however, both the input signal and the output signal of the unknown system are noisy (since both of them are microphone signals) and a different step size control is required. In this paper, the derivation of a new step size is presented. Since the resulting step size cannot be determined directly, an Artificial Neural Network (ANN)-based estimation of the step size is proposed and its effectiveness is demonstrated.
机译：归一化最小均方（NLMS）算法是用于线性系统识别的一种广泛使用的方法（例如，用于声学回声消除（AEC），需要估计扬声器和麦克风之间的声程）。一旦干扰源或背景噪声被激活，步长控制就成为一项关键任务，以确保快速而稳定的适应。常规的步长控制方法解决了附加噪声污染系统输出即麦克风信号的情况。但是，当将NLMS算法用于相对脉冲响应（RIR）估计时，未知系统的输入信号和输出信号都带有噪声（因为它们都是麦克风信号），因此需要不同的步长控制。在本文中，提出了新的步长的推导。由于无法直接确定最终的步长，因此提出了基于人工神经网络（ANN）的步长估计，并证明了其有效性。

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