## 1 Introduction
However, the correlation of acoustic emissions and weld-relevant parameters is imperceptible for human visual inspection [12]. Overall, existing studies indicate that the structure of signals should be considered for properly capturing their informativeness. For this purpose, measuring similarities and dissimilarities of signals is beneficial. This can be easily done by comparing signals with a representative one that corresponds to a weld-relevant parameter of interest. Nonetheless, computing or choosing a reference signal from the available data is prohibited as it is still unclear how a change in the welding process is expressed as a form of acoustic emissions. Furthermore, a lack of knowledge of these signals makes it also difficult to identify the outliers, and thus, using the mean of available data as a reference may not be reliable, if not misleading, especially when working with a limited number of samples. Considering our interests in classifying signal segments that represent weld-relevant parameters, handling them as a set is an effective alternative. For instance, if a signal segment is very similar to other segments of the same class and at the same time dissimilar to the segments of the other classes, then this particular segment can be regarded as highly informative. In this way, the informativeness of signal segments is represented by intraclass similarities and inter-class dissimilarities. This boils down to a cluster analysis, and the separability of different classes indicates how collectively informative those signal segments are. By performing classification subsequently, the inter-class separability can be numerically evaluated based on the resulting classification accuracy. As a result, the segment informativeness can be numerically represented. It is worth emphasizing that the term \"clustering\" in this work means general cluster analysis, including both supervised and unsupervised ones, instead of restricting it to unsupervised learning tasks. In an attempt to examine the feasibility of real-time acoustic monitoring and controlling systems for laser beam butt welding, this work investigates the relationship between signal duration and its informativeness. Specifically, the informativeness regarding the presence and size of butt joint gaps is examined for various segment durations. By identifying how much information is available for a particular signal duration, this work aims to provide a benchmark that can be utilized for the experimental design of developing such real-time systems. Here, the focus was set on the current challenge of monitoring high-power and high-speed laser welding, and our data were obtained accordingly. The contributions of this work are as follows: First, the informativeness of signal segments is numerically evaluated and compared for various durations. This was achieved by clustering signal segments that correspond to different gap sizes and running classification on the resulting clusters. Second, the performance is also compared among two sensor types, which are structure-borne and airborne ultrasound sensors, highlighting the advantages and drawbacks of these two sensor types under varying segment durations. Lastly, based on the findings from this study, further research steps are suggested to advance the realization of real-time acoustic monitoring and control systems.