Multichannel, audio processing approaches are widely examined in human–computer interaction, autonomous robots, audio surveillance, and teleconferencing systems. The numerous applications are linked to the speech technology and acoustic analysis area. Much attention is received to the active speakers and spatial localization of acoustic sources on the acoustic sensor arrays. Baseline approaches provide negotiable performance in a real-world comprised of far-field/near-field monitoring, reverberant and noisy environments, and also the outdoor/indoor scenarios. A practical system to detect defects in complex structures is the time difference mapping (TDM) technique. The significant scope of the research is to search the location using the minimum distance point in the time difference database to be apart from the verification point. In the case of the improved “time difference mapping (I-TDM)” technique and traditional “time difference mapping (T-TDM)” technique, the denser grids and vast database permit increased accuracy. In the database, if the location points are not present, then the accurate localization of the I-TDM and T-TDM techniques is damaged. Hence, to handle these problems, this article plans to develop acoustic source localization according to the deep learning strategy. The audio dataset is gathered from the benchmark source called the SSLR dataset and is initially subjected to preprocessing, which involves artifact removal and smoothing for effective processing. Further, the adaptive convolutional neural network (CNN)-based feature set creation is performed. Here, the adaptive CNN is accomplished by the improved optimization algorithm called distance mating-based red deer algorithm (DM-RDA). With this trained feature set, the acoustic source localization is done by the weight updated deep neural network, in which the same DM-RDA is used for optimizing the training weight. The simulation outcome proves that the designed model produced enhanced performance compared to other traditional source localization estimators.

中文翻译：

---

使用基于距离交配的红鹿算法开发自适应神经网络声源定位

多通道音频处理方法在人机交互、自主机器人、音频监控和电话会议系统中得到广泛研究。众多应用都与语音技术和声学分析领域相关。有源扬声器和声学传感器阵列上声源的空间定位受到了很多关注。基线方法在由远场/近场监控、混响和噪声环境以及室外/室内场景组成的现实世界中提供可协商的性能。检测复杂结构中的缺陷的实用系统是时差映射（TDM）技术。研究的重要范围是利用时差数据库中与验证点距离最小的点来搜索位置。在改进的“时差映射（I-TDM）”技术和传统的“时差映射（T-TDM）”技术的情况下，更密集的网格和庞大的数据库允许更高的精度。在数据库中，如果定位点不存在，则I-TDM和T-TDM技术的精确定位就会受到损害。因此，为了解决这些问题，本文计划根据深度学习策略开发声源定位。音频数据集是从称为 SSLR 数据集的基准源收集的，并首先进行预处理，其中包括伪影去除和平滑以进行有效处理。此外，执行基于自适应卷积神经网络（CNN）的特征集创建。这里，自适应 CNN 是通过称为基于距离交配的红鹿算法 (DM-RDA) 的改进优化算法来实现的。通过该训练后的特征集，声源定位由权重更新的深度神经网络完成，其中相同的 DM-RDA 用于优化训练权重。仿真结果证明，与其他传统的源定位估计器相比，所设计的模型具有增强的性能。