## 7 Concluding Remarks  
We proposed a dynamic generation system of impact sounds related to objects in a virtual environment, targeting a virtual space that reflects the real world. In the proposed system, the material label of the target object is first estimated based on the texture image of the object. For this purpose, the estimation model is trained using an existing dataset, in which the object image and the striking sound are labeled, and then the trained estimation model is subjected to transfer learning, according to the actual usage environment of the system. By doing so, it becomes possible to estimate the material label of the object that suits the environment assumed by the user. For the object image acquired from the real space, a unique texture image was acquired from the object image, including multiple parts, by performing material division by processing, based on pixel information and clustering. Next, a generative model was constructed using GAN to output a spectrogram image of the striking sound, with the material label output by the estimation model as input. With the proposed architecture, we demonstrated that better results, than the existing method, can be obtained by comparing the generated images. The generated spectrogram image is converted into sound data by the Griffin-Lim algorithm. By constructing a series of systems, from the acquisition of the texture image to the generation of the impact sound, it became possible to present the impact sound to the user for the object reflected from the real space to the virtual environment. In addition, by using GAN, we can present various impact sounds that are not limited to the sound data in the dataset. We conducted an experiment to evaluate the generated impact sound. First, we ran a preliminary experiment, using the sound data in the dataset, to determine whether it is possible to appropriately discriminate the impact sound for each material. After that, we performed the same experiment on the impact sound, generated by the proposed method, and compared the results with those of the preliminary experiment, which was the target value. We found that, although the proposed method showed a slight decrease in identification accuracy between materials, the results were similar to those in the preliminary experiment, depending on the combination of material labels presented. This demonstrates  
Figure 23: Comparison of results by Booming Index for each class. that our method is useful for obtaining feedback information on the interaction between the user and the object in the virtual environment. In this study, we used the, previously created, Greatest Hits dataset. This dataset includes images, material labels, and striking sounds for each object, so we presumed it was suitable for our purposes. However, the sounds in this dataset are from hitting an object with a wooden stick at a certain speed and not from hitting an object with other materials (e.g., bare hands) or from hitting it harder or faster. Therefore, it is necessary to create a dataset that collects the sounds of hitting objects of multiple materials in various situations, which is more suitable for the purpose of this research. In addition, we need to make adjustments, so that the sound data collected for creating the dataset can be sufficiently classified by the subjects using the texture images. Our future work will involve the creation of such a dataset, to enable more effective learning by the proposed method. Conceptualization, K.N. ; Data curation, K.K. and K.H. ; Methodology, K.K. and K.H. ; Project administration, K.N. ; Supervision, K.N. ; Writing--original draft, K.N., K.K. and K.H. ; Writing--review & editing, K.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. A publicly available dataset was analyzed in this study.