Automatic classification and reduction of wind noise in spectral data
Abstract
Outdoor acoustic data often include non-acoustic pressures caused by atmospheric turbulence, particularly below a few hundred Hz in frequency, even when using microphone windscreens. This paper describes a method for automatic wind-noise classification and reduction in spectral data without requiring measured wind speeds. The method finds individual frequency bands matching the characteristic decreasing spectral slope of wind noise. Uncontaminated data from several short-timescale spectra can be used to obtain a decontaminated long-timescale spectrum. This method is validated with field-test data and can be applied to large datasets to efficiently find and reduce the negative impact of wind noise contamination.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 01, 2021
- Source ID
- 10.1121/10.0005308
Entities
People
- Kent L Gee
- Mark K Transtrum
- Matt Calton
- Mylan R. Cook
- Shane V. Lympany
Organizations
- Brigham Young University
- United States Army