## 5 Experiments
### S2I Translation Results
#### 5.4.2 Qualitative Evaluation
For instance, regarding fireworks sounds, frames from the original training videos frequently contained white subtitles in the lower area, which ended  
Figure 11: Sharing of similar visual structure (and also color in some cases) between synthesized images and input-sound-corresponding images. Showing examples from the five sound classes: Baby cry—1 st row (**a**), 3 rd row (**b**); Dog—4 th row (**a**); Rail transport—3 rd row (**a**,**d**), 4 th row (**c**); Fireworks—1 st row (**b**,**d**), 2 nd row (**a**,**c**), 4 th row (**b**); and Water flowing—1 st row (**c**), 2 nd row (**b**,**d**), 3 rd row (**c**), 4 th row (**d**). S2I translation was performed from unknown sounds using a single translator model conditioned on 512-dimension audio embeddings. The babies’ faces in the original images were intentionally pixelated to preserve the children’s identities. up being modeled as part of the sound-emitting source. Consequently, these subtitles occasionally appear in generated images as horizontal luminous lines over a dark background (Figure 12, first row (d)). With respect to defective, incomplete, and artifactual images, such outputs occurred with different frequencies, occasionally significantly compromising the informativity of the translation. For instance, a typical defective image occurs when specific parts are inaccurately depicted, as seen in Figure 12 (first row (a)). Here, the baby's eye regions are brighter than the rest of the face, when usually the opposite is likely to happen. However, that which is clearly a bad output can also reveal the translator's ability to separately model different semantic components within the image. Another example of a defective image can be seen in Figure 12 (first row (e)), where the visual structure of the water scene seems to be upside down. And what could be the corner of a rocky beach with clear green water ends up looking like an abstract image. With respect to incomplete images, in the case of rail transport sounds, for instance, the translator may generate an interpretable image of a landscape which is coherent with the acoustic event in question. However, the absence of a train or railway makes the image incoherent (Figure 12, first row (c)). Another example of incomplete image is showcased in Figure 12 (first row (b)), where a potentially informative image of a dog is compromised due to the lack of elements indicating the location of the animal's head, resulting in a somewhat abstract image. Considering the entire dataset, abstract images occurred more frequently in the translation of dog sounds compared to other classes, but non-sense pictures may show up at any time. The most common outputs of this type are artifactual images, of which we show some examples in Figure 12 (second row (a to **e**)). _Model 'creativity'._ As mentioned in Section 3, since the translator must generate images based solely on an input sound, without any visual information about the original scene, we realized that the cross-modal generation performed in S2I translation implies addressing the problem of computational imagination. Despite the fact that our goal was to produce realistic images, there is an inherent 'creativity' required to enable the translator to 'imagine' complete scenes. Interestingly, we observed that the translator occasionally generated rather 'creative' results. While these outcomes were unintended, they were expected to happen, and they are an indicator of some level of 'creativity' achieved, since they demonstrate that the translator was capable of using learned visual features to generate original forms. Some of these 'creative' outputs included surreal, implausible, and creepy images, as illustrated in Figure 13. Sometimes the model's 'creativity' might have compromised the informativity of the image, while in other cases, it just added a bit of fantasy to the picture. With respect to implausible images, although they may occasionally be informative, the presence of awkward elements ends up diverting attention from relevant information.