# Automated classification of transient contamination in stationary acoustic data

### Abstract

An automated procedure for the classification of transient contamination of stationary acoustic data is proposed and analyzed. The procedure requires the assumption that the stationary acoustic data of interest can be modeled as a band-limited, Gaussian random process. It also requires that the transient contamination be of higher variance than the acoustic data of interest. When these assumptions are satisfied, it is a blind separation procedure, aside from the initial input specifying how to subdivide the time series of interest. No a priori threshold criterion is required. Simulation results show that for a sufficient number of blocks, the method performs well, as long as the occasional false positive or false negative is acceptable. The effectiveness of the procedure is demonstrated with an application to experimental wind tunnel acoustic test data which are contaminated by hydrodynamic gusts.

### Graphical abstract

## List of symbols

*B*Normalized signal bandwidth

*K*Kullback–Leibler divergence

*M*Mach number

*N*Number of samples in a block of data

*n*Sample index

*P*Probability distribution function

*p*Probability density function

*Q*Probability distribution function, estimate of

*P**q*Probability density function, estimate of

*p*- \(y_n\)
Individual sample in a block of data

- \(\alpha\)
Gamma distribution shape parameter

- \(\beta\)
Gamma distribution scale parameter

- \(\varGamma\)
Gamma function

- \(\gamma\)
Incomplete gamma function

- \(\nu\)
Effective degrees of freedom for signal of block size

*N*- \(\sigma ^2\)
Variance of a block of data

- \(\chi ^2_N\)
Sum of the squares of the samples in a block of data

## Notes

### Acknowledgements

The authors would like to acknowledge the support provided by the 14- by 22-Foot Subsonic Tunnel team, by colleagues in the Aeroacoustics and Advanced Sensing & Optical Measurements branches at the NASA Langley Research Center, and by colleagues in the Aerodynamics, Noise, and Propulsion Laboratory in the Boeing Test & Evaluation organization at The Boeing Company. The hybrid wing body test was funded by the NASA Environmentally Responsible Aviation Project.

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