## 1 Introduction
In addition, the literature on ICA has become extremely vast; however, there have been few reports on the identification of sources. It relies on the paradigm that a mixture of sources can be separated, provided that the sources bear different enough statistical properties. In a sense, the sources should have disjoint support sets in some statistical space. Some examples of sound signal separation are given in [17; 18], although they are closer to communication signal processing than the issue addressed in the present paper. [PERSON] et al. attempted to apply ICA to the fields of noise and vibration. They applied ICA to the inverse problem of noise to identify noise sources that overlap in both the spatial and frequency domains. Furthermore, they identified the noise source by applying ICA to a diesel engine, which is a complex structure [19; 20]. Moreover, [PERSON] et al. applied ICA to operational transfer path analysis (OTPA) and removed the crosstalk effect of the reference signal through ICA. The method in the study identified a more accurate delivery route than the existing OTPA method [21]. Thus, it is necessary to apply the ICA method to the existing contribution analysis method. The method of restoring independent signals will enable the contribution to be evaluated in situations in which it is difficult to apply the existing coherence analysis method. However, there are several problems to be solved in order to use ICA for coherence analysis. First, the number of input sources should be estimated. Noise is measured at several locations to estimate the noise source, which may not be where the noise is actually generated. To confirm the location, it is necessary to estimate the number of input sources to determine exactly where the noise is generated. Minka Bayesian Model Selection (MIBS) can be used to accurately separate signals only by determining the correct number of sources [22]. Second, the labeling of signals should be performed. When a signal is separated using ICA, it is difficult to determine which signal originated from which location because each signal is not labelled. In order to identify the noise source, it is necessary to determine where the separated signal originated. The rest of the paper is devoted to proposing solutions to these issues. This study intends to identify the exact location of the seat rattle noise of vehicles. For this purpose, an ICA-based coherence technique was proposed. Section 2 deals with the coherence analysis method, the theory of ICA, and the performed numerical simulations. Section 3 applies the proposed method to the seat rattle noise. Furthermore, it discusses the estimation of the number of input sources, labeling of signals, and size restoration to utilize the ICA method in the coherence evaluation method. Finally, it identifies the location of the seat rattle noise of the vehicle using an ICA-based contribution analysis method. Thus, in this paper, the ICA-based coherence technique is proposed by improving the coherence method. In order to apply the ICA to the coherence evaluation, the following problems were solved: the restoration of signal amplitude, the estimation of the number of signals, and the labeling of signals.