Scrubbing Complex Sound Sources for Factory Situational Awareness

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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.