论文标题
多指数音能衰减分析的神经网络
Neural network for multi-exponential sound energy decay analysis
论文作者
论文摘要
声音能量衰减功能（EDF）的已建立模型是多个指数和噪声项的叠加。这项工作提出了一种基于神经网络的方法，用于估计EDF的模型参数。该网络对合成EDF进行了培训，并在两个在各种声学环境中进行的20000多个EDF测量的大数据集进行了评估。评估表明，所提出的神经网络体系结构可靠地估算来自测量的EDF的大数据集的模型参数，同时轻巧且计算有效。提出的神经网络的实施已公开可用。
An established model for sound energy decay functions (EDFs) is the superposition of multiple exponentials and a noise term. This work proposes a neural-network-based approach for estimating the model parameters from EDFs. The network is trained on synthetic EDFs and evaluated on two large datasets of over 20000 EDF measurements conducted in various acoustic environments. The evaluation shows that the proposed neural network architecture robustly estimates the model parameters from large datasets of measured EDFs, while being lightweight and computationally efficient. An implementation of the proposed neural network is publicly available.
