I-Corps: Embedded Machine Listening for Smart Acoustic Monitoring

I-Corps项目的更广泛的影响/商业潜力是使用嵌入式机器监听作为一种低成本的、可交付的解决方案，用于早期检测机器故障并改善预测性维护。在制造业中，基于声音的状态监测与数据驱动的维护相结合，可以显著减少计划外停工、产品故障和原材料浪费。通过集成HVAC设备、电梯、锅炉和泵等关键机械的实时状态更新，可以增强建筑物管理系统，最大限度地减少对这些服务的管理人员和用户的干扰。该技术灵活、准确、数据驱动，为潜在客户提供了较低的采用门槛，并可适应各种市场。除了预测性维护，应用还包括噪音水平监测，以确保工作场所和机场的合规性，家庭和建筑安全，交通事故的早期预警，动物物种的生物声学监测，以及大规模的室外噪音监测，以改善智能城市的执法。这个I-Corps项目进一步发展了人工智能和物联网的交叉研究。该技术由经过校准的高精度声学传感器和基于深度学习的嵌入式声音识别人工智能组成。声音传达了关于环境的重要信息，而这些信息通常无法通过其他手段来测量。在制造业中，早期机械故障可以通过异常声发射来指示。在智能家居和建筑中，可以监控声音，以识别警报、遇险或合规的迹象。声音传感是全方位的，对遮挡和上下文变量（如一天中不同时间的闪电条件）具有鲁棒性。许多现有的解决方案不能识别不同类型的声音或复杂的声学模式，使它们不适合这些应用。该解决方案成本低，并且能够识别网络边缘的事件和源，从而消除了传输敏感音频信息的需要。

The broader impact/commercial potential of this I-Corps project is the use of embedded machine listening as a low-cost, turnkey solution for early detection of machinery malfunction and improve predictive maintenance. In manufacturing, sound-based condition monitoring coupled with data-driven maintenance can help significantly reduce unscheduled work stoppages, faulty products and waste of raw materials. Building management systems can be augmented by integrating real-time condition updates for critical machinery such as HVAC units, elevators, boilers and pumps, minimizing disruption for managers and users of those services. This technology is flexible, accurate and data-driven, potentially providing a low barrier to adoption for prospective customers and adaptability to various markets. Beyond predictive maintenance, applications include noise level monitoring for ensuring compliance in workplaces and airports, home and building security, early alert for traffic accidents, bio-acoustic monitoring of animal species, and outdoor noise monitoring at scale for improved enforcement in smart cities.This I-Corps project further develops research at the intersection of artificial intelligence and the internet of things. The technology consists of a calibrated and highly accurate acoustic sensor with embedded sound recognition AI based on deep learning. Sound conveys critical information about the environment that often cannot be measured by other means. In manufacturing, early stage machinery malfunction can be indicated by abnormal acoustic emissions. In smart homes and buildings, sound can be monitored for signs of alarm, distress or compliance. Sound sensing is omnidirectional and robust to occlusion and contextual variables such as lightning conditions at different times of the day. Many existing solutions cannot identify different types of sounds or complex acoustic patterns, making them unsuited for these applications. This solution is both low cost and capable of identifying events and sources at the network edge, thus eliminating the need for sensitive audio information to be transmitted.