Mirabilii D, Lakshminarayana KK, Mack W, Habets E (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2020-May
Pages Range: 576-580
Conference Proceedings Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Event location: Barcelona, ESP
ISBN: 9781509066315
DOI: 10.1109/ICASSP40776.2020.9054381
A deep neural network (DNN) based approach for estimating the speed of airflows using closely-spaced microphones is proposed. The spatial characteristics of wind noise measured with a smallaperture array are exploited, i.e., the low-frequency spatial coherence of wind noise signals is used as an input feature. The output is an estimate of the wind speed averaged over a specific time interval. The DNN is trained using synthetic wind noise, which overcomes the time-consuming data collection and allows to isolate wind noise from different acoustic sources. The dataset used for testing comprises wind noise measured outdoors with a circular linear array and a ground truth obtained using an ultrasonic anemometer. The obtained model is applied to generated and measured wind noise. The performance of the proposed method is assessed across a wide range of wind speeds and directions, using different time resolutions.
APA:
Mirabilii, D., Lakshminarayana, K.K., Mack, W., & Habets, E. (2020). Data-Driven Wind Speed Estimation Using Multiple Microphones. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 576-580). Barcelona, ESP: Institute of Electrical and Electronics Engineers Inc..
MLA:
Mirabilii, Daniele, et al. "Data-Driven Wind Speed Estimation Using Multiple Microphones." Proceedings of the 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, Barcelona, ESP Institute of Electrical and Electronics Engineers Inc., 2020. 576-580.
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