RU2021135404A - METHOD AND DEVICE FOR DETERMINING CHARACTERISTICS OF AIR FLOW - Google Patents

Claims (37)

1. A method for determining the characteristics of an air flow, comprising the steps of: receive acoustic signals generated by the air flow, through an array (100) of microphones; extracting characteristic information from the acoustic signals; determining airflow information based on the characteristic information; wherein the airflow information contains information regarding the wind speed

and/or wind direction

; wherein the determination of the information is based on the correlation of the characteristic information extracted from the acoustic signals with an expected version of the corresponding characteristic information; wherein the expected version is determined using a Korkos model, a specialized model, or another model; or wherein the step of determining the information is based on regression or classification of the characteristic information. 2. The method according to claim 1, wherein the characteristic information comprises temporal information and/or spectral information and/or spatial information and/or a feature. 3. The method according to claim 1 or 2, wherein the expected version is determined using the Korkos model, which is described by the following formula:

Where

denotes the discrete angular frequency, K and

denote the length of the discrete Fourier transform and the sampling rate, respectively, d denotes the distance between the microphones,

denotes the speed of convective turbulence,

denotes the coherence damping parameter. 4. The method according to paragraphs. 1, 2, or 3, wherein the step of determining the airflow information comprises calculating an optimal set

by solving the least squares (LS) minimization problem

where timeframe l has been omitted for brevity,

denotes the measured spatial coherence,

denotes a theoretical model. 5. The method of claim 4, wherein the step of determining the airflow information comprises: minimizing the Frobenius norm of the composite error matrix given by the difference between the measured spatial coherence matrix and the matrix determined by the Korkos model; and/or wherein the step of determining the airflow information comprises calculating

Where

is the estimated wind propagation vector,

is the square of the Frobenius norm of the matrix, and

defined as

, Where

- matrix of the measured spatial coherence calculated on the basis of acoustic signals. 6. A method according to one of the preceding claims, wherein the feature information is extracted by using a supervised machine learning approach and/or a deep learning approach, and/or by using a convolutional or fully connected neural network. 7. A method according to one of the preceding claims, wherein the determination of the airflow information is performed by comparing the features obtained with measurements of the characterized airflow. 8. The method according to one of the preceding claims, further comprising isolating wind noise from the received acoustic signal. 9. The method of claim 8, wherein the step of isolating wind noise comprises selecting a frequency range of the acoustic signal, wherein the frequency range selection is in the range of 20 Hz to 20 kHz, the selection of a frequency range below 2 kHz, below 1.5 kHz, and/ or in which the wind noise isolation step comprises filtering. 10. The method of claim 9, wherein the step of isolating the wind noise further comprises the step of estimating a frequency range of the wind noise, said frequency range being selected. 11. The method of claim 9, wherein the step of isolating wind noise comprises converting the acoustic signals into a time-frequency domain or other domain. 12. The method of one of the preceding claims, the method further comprising post-processing to remove outliers. 13. A computer program having program code for performing the method of one of the preceding claims when executed by a computer. 14. Device (100) for determining the characteristics of the air flow, containing: an array (100) of microphones for receiving acoustic signals generated by the air flow; an acoustic signal analysis unit (30) configured to extract characteristic information from the acoustic signal; And an evaluation unit (70) configured to determine airflow information based on the characteristic information; wherein the airflow information contains information regarding the wind speed

and/or wind direction

; wherein the determination of the information is based on the correlation of the characteristic information extracted from the acoustic signals with an expected version of the corresponding characteristic information; wherein the expected version is determined using a Korkos model, a specialized model, or another model; or wherein the step of determining the information is based on regression or classification of the characteristic information. 15. The apparatus (100) of claim 14, wherein the microphone array (100) comprises at least three microphones. 16. Device (100) according to claim 14 or 15, wherein the microphone comprises a plurality of microphones that are less than 20 mm apart, or less than 15 mm, or less than 10 mm, or less than 30 mm apart. 17. The device (100) according to one of paragraphs. 14-16, wherein the microphone array (100) comprises a plurality of microphones that are placed in a planar constellation and/or that are installed in a free field. 18. The device (100) according to one of paragraphs. 14-17, wherein the device (100) comprises a low pass filter (2) or conversion element configured to isolate wind noise from the acoustic signal. 19. The device (100) according to one of paragraphs. 14-18, wherein the device (100) further comprises a post-processing unit (5) configured to remove outliers.