Classification of environmental noise sources using machine-learning methods
| Autor: | Dan Valente, Edward T. Nykaza, Matthew G. Blevins, Arnold P. Boedihardjo, Andrew Hulva |
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| Rok vydání: | 2015 |
| Předmět: |
Acoustics and Ultrasonics
Artificial neural network Noise measurement business.industry Computer science computer.software_genre Machine learning Noise Deep belief network Arts and Humanities (miscellaneous) Principal component analysis Data mining Artificial intelligence Environmental noise business Cluster analysis computer |
| Zdroj: | The Journal of the Acoustical Society of America. 138:1731-1731 |
| ISSN: | 0001-4966 |
| DOI: | 10.1121/1.4933450 |
| Popis: | Unattended and continuously running environmental noise monitoring systems can capture an intractable amount of data. The signals captured can include a multitude of sources (e.g., wind noise and anthropogenic noise sources) in addition to the environmental noise sources of interest (e.g., aircraft, vehicles, trains, and military weapons). In this presentation, we explore the use of machine-learning methods to effectively isolate and identify environmental noise sources captured on such a noise monitoring system. Specifically, we consider the use of both unsupervised (e.g., principle components analysis, clustering methods, and deep belief networks) and supervised (e.g., logistic regression, support vector machines, and neural networks) pattern-learning methods to derive the features of interest and classify the signals based on the obtained features. The generalization performance of each method is assessed using a dataset of over 120,000 human classified signals, and the strengths and weaknesses of each approach are discussed. |
| Databáze: | OpenAIRE |
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