Soundscape augmentation is an emerging approach for noise mitigation by introducing additional sounds known as "maskers" to increase acoustic comfort. Traditionally, the choice of maskers is often predicated on expert guidance or post-hoc analysis which can be time-consuming and sometimes arbitrary. Moreover, this often results in a static set of maskers that are inflexible to the dynamic nature of real-world acoustic environments. Overcoming the inflexibility of traditional soundscape augmentation is twofold. First, given a snapshot of a soundscape, the system must be able to select an optimal masker without human supervision. Second, the system must also be able to react to changes in the acoustic environment with near real-time latency. In this work, we harness the combined prowess of cloud computing and the Internet of Things (IoT) to allow in-situ listening and playback using microcontrollers while delegating computationally expensive inference tasks to the cloud. In particular, a serverless cloud architecture was used for inference, ensuring near real-time latency and scalability without the need to provision computing resources. A working prototype of the system is currently being deployed in a public area experiencing high traffic noise, as well as undergoing public evaluation for future improvements.
翻译:声音增强是一个新出现的减少噪音的方法,通过增加被称为“ makers” 的音响来增加声响的舒适度。 传统上, 遮罩器的选择往往以专家指导或热后分析为基础, 这可能耗费时间, 有时是任意的。 此外, 这往往导致一组静态的遮罩器与现实世界声学环境的动态性质不灵活。 克服传统声光增强的不灵活性是双重的。 首先, 系统必须能够在没有人类监督的情况下选择一个最佳遮罩器。 其次, 系统还必须能够对声学环境的变化作出反应, 近实时的悬浮度。 在这项工作中, 我们利用云计算和事物互联网的组合优势( IoT) 来使用微控制器进行现场监听和回播放, 同时将计算成本昂贵的推断任务下放给云层。 特别是, 一个没有服务器的云层结构被用于推断, 确保接近实时的隐蔽和缩放性, 而不需要提供计算资源。 在这项工作中, 一个运行的系统原型模型正在被部署, 进行公共循环, 正在不断 进行 进行 。