We report a detection method of an acoustic source by the convolutional neural network (CNN) that utilizes analytic predictions of sound radiation. The analytic predictions with various source conditions are implemented to effectively collect a large-annotated training dataset, allowing straightforward utilizations of the CNN in the acoustic domain. The data conversion from the synthetic audio signals into the pseudo-images is presented to secure compatibility with actual audio signals in terms of the direction of angle (DOA) estimation. Our source localization network fully trained with the synthetic pseudo-images were verified with various source conditions in a semi-reverberant room. The verifications demonstrate remarkable robustness and noise resistance to estimate the DOA regardless of source conditions. Moreover, considering our network has implemented a small number of short-time audio signals (i.e., three audio signals for 0.1 s), the proposed algorithm can be a breakthrough in a real-time tracking of the acoustic source by hybridizing analytical and data-driven approaches.

我们报告了利用卷积神经网络（CNN）利用声辐射的解析预测的声源检测方法。实施具有各种源条件的分析预测以有效地收集带有注释的大型训练数据集，从而可以在声域中直接使用CNN。提出了从合成音频信号到伪图像的数据转换，以确保在角度方向（DOA）估计方面与实际音频信号兼容。我们的源定位网络经过全面的合成伪图像训练，并在半混响室内通过各种源条件进行了验证。验证显示出了卓越的鲁棒性和抗噪声性，无论源条件如何，都可以估算DOA。而且，