The authors focus their attention on the analysis of the probability density function of the equivalent noise level, in the context of a determination of the uncertainty of the obtained results in regard to the control of environmental acoustic hazards. In so doing, they discuss problems of correctness in the applicability of the classical normal distribution for the estimation of the expected interval value of the equivalent sound level. The authors also provide a set of procedures with respect to its derivation, based upon an assumption of the determined distribution of the measurement results. The obtained results then create the plane for the correct uncertainty calculation of the results of the determined controlled environmental acoustic hazard coefficient.

The specific working conditions of the wind turbine in strong wind cause a number of problems in the measurement of noise indicators used in its short and long-term assessment. The wind is a natural working environment of the turbine, but it also affects the measurement system, moreover, it can be a secondary source of other sounds that interfere with the measurement. One of the effective methods of eliminating the direct impact of wind on the measurement system is placing the microphone on the measurement board at ground level. However, the obtained result can not be directly compared with the admissible values, as it has to be converted to a result at a height of 4 m. The results of previous studies show that this relation depends, inter alia, on the speed and direction of the wind. The paper contains the results of measurements on the measurement board, according to EN 61400-11:2013, and at a height of 4 m above ground made simultaneously in three points around the 2 MW turbine at various instantaneous speeds and changing wind directions. Analysis of the impact of measuring point location on the measurement result of noise indicators and the occurrence of additional features affecting the relationship between the values measured on the board and at the height of 4 m, and especially the tonality, amplitude modulation and content of low frequency content, was m

Assessment of several noise indicators are determined by the logarithmic mean <img src="/fulltext-image.asp?format=htmlnonpaginated&src=P42524002G141TV8_html\05_paper.gif" alt=""/>, from the sum of independent random results L1; L2; : : : ; Ln of the sound level, being under testing. The estimation of uncertainty of such averaging requires knowledge of probability distribution of the function form of their calculations. The developed solution, leading to the recurrent determination of the probability distribution function for the estimation of the mean value of noise levels and its variance, is shown in this paper.

The acoustic vector sensor (AVS) is used to measure the acoustic intensity, which gives the direction-ofarrival (DOA) of an acoustic source. However, while estimating the DOA from the measured acoustic intensity the finite microphone separation (d) in a practical AVS causes angular bias. Also, in the presence of noise there exists a trade off between the bias (strictly increasing function of d) and variance (strictly decreasing function of d) of the DOA estimate. In this paper, we propose a novel method for mitigating the angular bias caused due to finite microphone separation in an AVS. We have reduced the variance by increasing the microphone separation and then removed the bias with the proposed bias model. Our approach employs the finite element method (FEM) and curves fitting to model the angular bias in terms of microphone separations and frequency of a narrowband signal. Further, the bias correction algorithm based on the intensity spectrum has been proposed to improve the DOA estimation accuracy of a broadband signal. Simulation results demonstrate that the proposed bias correction scheme significantly reduces the angular bias and improves the root mean square angular error (RMSAE) in the presence of noise. Experiments have been performed in an acoustic full anechoic room to corroborate the effect of microphone separation on DOA estimation and the efficacy of the bias correction method.