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In many countries, the acoustic impact of wind farms is often constrained by a curtailment plan to limit their noise, which spreads in their surroundings. To update the plan, on/off cycle measurements are performed to determine the ambient noise (wind turbines in operation) and residual noise (wind turbines shut down), but these shutdown operations are limited in time, which reduces the representativeness of the estimated in situ emergence. Consequently, a machine learning technique, called nonnegative matrix factorization (NMF), is proposed to estimate the sound emergence of wind turbines continuously, i.e., without stopping the machines. In the first step, the application of NMF on a corpus of various simulated scenes allows the determination of the optimal setting of the method to better estimate the sound emergence. The results show the proper adaptation of the method with regard to the influence of the propagation distance and atmospheric conditions. This method also proves to be efficient in cases in which the real emergence is less than 5 dB(A) with a mean error lower than 2 dB(A). The first comparison with in situ measurements validates these performances and allows the consideration of the application of this method to optimize wind farm operations.

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