@article {Wang:2021:0736-2935:4962,
title = "Scalable Machine Learning Approach to Classifying Transportation Noise at Two Urban Sites in Greater Boston, Massachusetts",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2021",
volume = "263",
number = "1",
publication date ="2021-08-01T00:00:00",
pages = "4962-4974",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2021/00000263/00000001/art00006",
doi = "doi:10.3397/IN-2021-2907",
author = "Wang, Tiange and Jiang, Ruijie and Lin, YuLun (Elain) and Monahan, Kyle and Leaffer, Douglas and Doroff, Stephen and Tracey, Brian",
abstract = "The goal of this study was to characterize transportation noise by vehicle class in two urban communities, to inform studies of transport noise and ultra-fine particulates. Data were collected from April to September 2016 (150 days) of continuous recording in each urban community using
high-resolution microphones. Training data was created for airplanes, trucks/buses, and train events by manual listening and extraction of audio files. Digital signal processing using STFT and Hanning windowing was performed in MATLAB, creating audio spectrograms with varying frequency: log
vs linear frequency scales, and 4K vs 20K max frequency. For each of the four spectrogram sets, a neural net model using PyTorch was trained via a compute cluster. Initial results for a multi-class model provide an accuracy of 85%. Comparison between a selection of frequency scales and expanding
to longer time periods is ongoing. Validation with airport transport logs and local bus and train schedules will be presented.",
}