Trailing-edge noise can be the dominant noise source of diverse industrial applications, including wind turbines. Noise regulations may limit the power production and installation of new wind farms. There is a need to reduce it. In order to achieve so, we first need to predict it, and to do so we need experimental data to support and validate the new methodologies. A new experimental campaign has been carried out to provide a benchmark of trailing-edge noise applicable to wind turbine noise. It involves a broad range of Reynolds numbers and angles of attack, and covers the noise reduction effect of serrations. This Thesis studies the aforementioned novel data set. The trends of the far-field noise with the forcing of the boundary layer, angle
of attack, and Reynolds number have been studied. Furthermore, scaling laws have been applied to compare different test conditions. The collapse quality has been studied, and the differences have been linked to the aerodynamics and possible post-processing effects. The effect of the serrations has also been studied. Additionally, the noise reduction dependence on the aerodynamic loading is discussed. The influence of the microphone locations on the far-field spectra uncertainty is also studied. The Monte-Carlo method has been applied using the Delay-and-Sum and Clean-SC beamformers. The effect of the input uncertainty correlation, wind tunnel velocity, facility, and algorithm are examined.