## 1 Introduction
To cancel industrial noise from speech signal, EMD was applied to generate the components of signal and the selection of IMFs for signal regeneration is optimized by using objective function of the intelligent particle swarm optimization algorithm (PSO) [23]. Then, the EMD denoising procedure inspired by wavelet threshold was proposed and achieved some preliminary outcomes. However, the direct application of wavelet threshold into EMD was inappropriate in principle and could result in incontinuity of the reconstructed signal [24]. Immediately afterwards, the concept of interval threshold denoising was introduced to overcome the problem and was widely applied in the noise cancellation process. In [21], [PERSON] et al. compared Fourier Bessel (FB) expansion-based and EMD-FB-based algorithms to denoise underwater acoustic signals as the sea water usually contained unwanted background noise. In [25], an adaptive thresholding denoising technique by employing ensemble empirical mode decomposition (EEMD) and the intelligent genetic algorithm (GA) was applied for electrocardiogram signals. The experimental results indicated that the proposed denoising method outperformed other denoising methodologies in terms of signal-to-noise ratio, mean square error and percent root mean square difference. The fruit fly optimization algorithm (FOA) was proposed by [PERSON] in 2012 Pan (2012). FOA is a meta-heuristic optimization method simulating the intelligent foraging process of the fruit fly swarm. The natural fruit fly has superior ability in the senses of olfactory and vision system compared with other species. It can even smell food odor from as far as 40 km away and locate other flocks by its sensitive vision. Compared with other existing meta-heuristics, FOA involves an easily understandable principle, a simple structure and fewer parameters. Since the FOA was proposed, it has been widely applied in parameter optimization Pan (2012), machine scheduling Pan (2012), and pattern recognition Pan (2012), etc. The FOA searches the optimal solution only in the neighborhood of previous group location, which indicates that the location of each fruit fly during the iteration process is determined according to previous swarm coordinate and the preset fly distance range. The optimization is therefore simplified furthest to decrease the convergence time and enhance the local searching speed. Excessive pursuit of local searching ability has also reduced the diversity of the fruit fly swarm. Many researchers point out that the basic FOA can easily to fall into local extreme due to poor global searching capacity [PERSON] (2013). In [PERSON] (2012), a genetic proportion coefficient was introduced into the basic FOA to enhance the global searching ability. A simulation example, with the cutting pattern clustering accuracy of 95%, and some comparisons, verified the effectiveness and superiority of the proposed method and an industrial test proved its practical effect. In [PERSON] (2013), an ensemble parameter FOA (EPFOA) was proposed to improve the precision and robustness of complex parts quality inspection. The proposed algorithm was superior to other algorithms in terms of accuracy and robustness by some comprehensive comparisons with other registration methods and some variants of FOA. [PERSON] et al. adopted a new linear generation mechanism of candidate solution to enhance the searching efficiency and improve the searching quality of basic FOA [PERSON] (2013). Motivated by the above-mentioned observations, an adaptive EMD threshold denoising method for acoustic signals is proposed in this paper. The FOA is introduced into the threshold determination process of the denoising approach. In order to balance the local and global searching ability, a variation coefficient and a disturbance coefficient are added into the basic FOA. The rest of this paper is organized as follows. In Section 2, the basic EMD noise elimination method and optimization process of FOA are presented.