###### Abstract
In particular, this is because most of the aerodynamic and performance tests are carried out in the non-anechoic indoor test bed, and not all aeroacoustic tests can be done in a perfect anechoic chamber. There exists very strong background noise which can contaminate the LE and TE noise in these tests. Therefore, it would be critical to separate and quantify the airfoil LE and TE noise source to get the useful aeroacoustic information. Acoustic beamforming using a phased microphone array is a widely used method and powerful technique to characterize and localize the flow-induced noise. The performance of the beamforming mainly depends on the array distribution and the data reduction method. The accurate strength of the sound source can be determined due to the recent development of the inverse method [31; 32; 33; 34; 35]. Deconvolution methods, which aim to identify the point spread functions (PSF) of source maps, may fail when the actual beam patterns of the sound source are not similar to the synthetic PSF. To resolve this problem, a new deconvolution method named Clean based on spatial source coherence (Clean-SC) is developed. The side lobes can be removed using the Clean-SC algorithm. One important advantage of the Clean-SC method is that it can be used to extract the absolute sound levels from the sound maps [36]. This study concerns the quantitative evaluation of the blade TE noise reduction with serrated TEs based on the Clean-SC algorithm. The computing software for microphone array data processing is developed and assessed by [PERSON]'s benchmark case. A linear array with 32 microphones is employed to identify the LE noise and TE noise of a blade. The TE noise for the blade with and without sawtooth configurations is measured with various inflow velocities. Parametric studies of the sawtooth amplitude and wavelength on TE noise reduction are performed to systematically explore the noise reduction performance. The Assessment of the Array Data Processing Software Using [PERSON]'s Benchmark Synthesized Input Data  
The benchmark synthesized input data proposed by [PERSON] et al. [37] are used to assess the present array data reduction program which is based on the Clean-SC algorithm. The Clean-SC is an improved version of the classical deconvolution method Clean [38] employed in Astronomy. It was proposed to overcome the disadvantages of PSF-based methods and it takes advantage of the fact that the main-lobes are spatially coherent with their side-lobes. The beam pattern of each noise source was determined by solvingthe measured spatial coherence rather than by using the synthetic PSF's. Better noise identification results might be achieved by Clean-SC compared with other deconvolution methods such as DAMAS [34], due to the fact that Clean-SC does not assume theoretical beam patterns. In addition, Clean-SC is an efficient deconvolution method which takes only twice the data-processing time for conventional beamforming [36]. The benchmark synthesized input data include four monopole sound sources located at the four corners of a square with dimensions of 0.2 m \(\times\) 0.2 m. The sources are 0.75 m away from the planar array with sixty-four microphones. The exact coordinates of the sources are, respectively, 0.1 m, \(-\)0.1 m, 0.75 m; \(-\)0.1 m, \(-\)0.1 m, 0.75 m; \(-\)0.1 m, 0.1 m, 0.75 m and 0.1 m, 0.1 m, 0.75 m. As shown in Figure 1, the planar array has approximately a 1.5 m aperture with seven spiral arms. We evaluate two subcases in this study. For subcase A, the four sources possess the same sound intensity. For subcase B, the four sources possess different intensity with the other three sources respectively having sound levels of 6 dB, 12 dB and 18 dB less than the most intensive source. As indicated by [PERSON] et al. [37], unrelated random number generators are employed for noise source generation. Consequently, the four sources contribute uncorrelated sound signals to each microphone.