Acoustic Event Detection Using Machine Learning: Identifying Train Events
نویسندگان
چکیده
Light-rail systems are becoming more popular in cities and urban residential areas around the country. One of the main environmental impacts from light-rail systems is noise from the trains as they pass through residential areas. In response to increasing noise complaints, it is becoming more common to perform noise measurements in the residential areas and attempt to identify noise mitigation solutions based on the results. Currently, most of the noise measurements are attended with a technician keeping a log of when trains pass the measurement location so those train events can be extracted from the continuous recording during post-processing. This method limits the amount of data that can be collected due to limited man hours. A machine learning algorithm that identifies train events in a continuous noise recording would increase efficiency during both data collection and post-processing.
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