This paper elucidates a model for acoustic single and multi-tone classification in resource constrained edge devices. The proposed model is of State-of-the-art Fast Accurate Stable Tiny Gated Recurrent Neural Network. This model has resulted in improved performance metrics and lower size compared to previous hypothesized methods by using lesser parameters with higher efficiency and employment of a noise reduction algorithm. The model is implemented as an acoustic AI module, focused for the application of sound identification, localization, and deployment on AI systems like that of an autonomous car. Further, the inclusion of localization techniques carries the potential of adding a new dimension to the multi-tone classifiers present in autonomous vehicles, as its demand increases in urban cities and developing countries in the future.

翻译：本文阐述了资源受限边缘装置的单一声调和多声调分类模型,拟议的模型是最新先进快速精确稳定微小配置常规神经网络模型,通过使用效率较高、使用减少噪音算法的较低参数,改进了性能计量标准,使规模低于以往的假设方法;该模型作为声学独立软件模块实施,重点是应用声学识别、本地化和在像自主汽车那样的光学系统中部署;此外,采用本地化技术有可能给自治车辆中存在的多声调分类器增加新的层面,因为城市和发展中国家今后的需求会增加。