Scrubbing Complex Sound Sources for Factory Situational Awareness

根据疾控中心NIOSH网站的说法，职业性听力损失是最常见的 在美国，与工作有关的疾病。这在一定程度上是由于工人的数量 在工作场所暴露在危险噪音水平下，不使用听力保护设备 (HPD)。美国国家卫生研究所的一项调查估计，超过2200万美国工人 每年在工作中暴露在危险噪音水平下。另一项调查显示，超过一半的人 工人们报告说没有使用HPD。在工厂车间不使用HPD通常不是因为 此类设备的成本或缺乏可用性。在许多情况下，可以看到耳塞和其他HPD 挂在工人的脖子上或塞进口袋里！问题是，除了 HPD能屏蔽有害声音，还能屏蔽环境中除感知之外的所有其他声音 警报对于这些行业的人身安全和工作表现至关重要。我们提出了一个 新的听力保护系统，提供增强的访问“情景”声音 作为警报、叉车和声音，同时抑制危险的嘈杂噪音。通过 克服情境感知障碍，我们相信员工将更愿意穿着 包括我们的系统的HPD。 我们的解决方案使用创新的信号处理技术来“擦洗”危险的声音 每台机器都被相邻的麦克风(MIC)“标记”，从回应中 其他“情景”麦克风的混合体，放置在 工作区。该洗涤过程从响应信号中去除标记噪声 情景麦克风，从而增强了声音中其他重要声音的可听性 环境。与现有产品不同的是，这种擦洗与频率或 被擦除的信号的幅度。 基于清除小来源噪声的成功结果，该项目重点关注两个 我们当前系统的局限性：1)用作标记麦克风的声学传感器可根据情况进行拾取 除了不想要的噪音外，还有其他声音。这种标记器污染使其更难 只清除情景麦克风发出的不需要的噪音。我们建议研究替代传感器 减少污染的技术。2)噪音较大的机器通常有多个底层 噪声源(例如马达、滑轮、轴承)和相对较大的壳体 消息来源。成功的擦洗需要与不同的噪声源一样多的标记麦克风。我们 建议开发和测试一个程序，以确定标记麦克风的数量和位置 需要擦洗具有多个内部来源的机器。

According to the CDC NIOSH website “Occupational hearing loss is one of the most common work-related illnesses in the United States.” This is due, in part, to the number of workers exposed to hazardous noise levels in the workplace who don’t use hearing protection devices (HPDs). A National Health Institute Survey estimated that over 22 million U.S. workers are exposed to hazardous noise levels at work annually. Another survey indicated over half of those workers reported non-use of HPDs. Non-use of HPDs on factory floors is often not due to the cost of such devices or lack of availability. In many cases, ear plugs and other HPDs can be seen hanging around a worker’s neck or stuffed in a pocket! The problem is that, in addition to blocking hazardous sounds, HPDs also block all other sounds in the environment but awareness of alarms is critical for physical safety and job performance in these industries. We propose a novel hearing protection system that provides enhanced access to “situational” sounds such as alarms, forklifts, and voices, while simultaneously suppressing hazardously loud noises. By overcoming the situational-awareness obstacle, we believe workers will be more willing to wear HPDs that include our system. Our solution uses innovative signal processing technologies to “scrub” hazardously loud machines, each of which has been “tagged” by adjacent microphones (mics), from the response mixtures of other, “situational” mics placed in more acoustically diverse locations in the workspace. This scrubbing process removes the tagged noises from the response signals of the situational mics, thereby enhancing the audibility of the other important sounds in the environment. Unlike existing products, this scrubbing is independent of the frequency or amplitude of the signal being scrubbed. Based on successful results scrubbing noise from small sources, this project focuses on two limitations of our current system: 1) Acoustic sensors used as tagging mics pick up situational sounds in addition to the unwanted noise. This tagger contamination makes it more difficult to scrub just unwanted noise from situational mics. We propose to investigate alternative sensor technologies to reduce contamination. 2) Noisy machines typically have multiple underlying noise sources (e.g. motors, pulleys, bearings) and relatively large shells encasing the noise sources. Successful scrubbing requires as many tagging mics as distinct noise sources. We propose to develop and test a procedure to identify the number and location of tagging mics needed to scrub a machine with multiple internal sources.