## 5 Experiments
### S2I Translation Results
#### 5.4.2 Qualitative Evaluation
However, we could find exceptions to this rule, and even images translated from unknown sounds may occasionally resemble scenes corresponding  
Figure 9: Qualitative demonstration of results among the five sound classes, from top to bottom row: Baby cry, Dog, Rail transport, Fireworks, and Water flowing. S2I translation was performed from unknown sounds using a single translator model conditioned on 512-dimension audio embeddings. to the input sound. In Figure 11, we present a selection of translations that produced such a result. Using the currently evaluated model, in most cases presented, the translated images share the original visual structure and also the color, but sometimes the generated image has a mirrored structure compared to the original one (Figure 11, first row (a) on top and third row (d)). Also, the translated image may match the original visual structure, but colors and textures are different, like the aforementioned mirrored image (Figure 11, first row (a)) which pictures a baby crying and the dog image (fourth row (a)), in which, despite having completely different backgrounds, the blurry silhouette in the translated image shows the dog in a quite similar pose when compared to its original pair. Some of the remaining examples exhibit unexpected visual matching in Rail transport, Fireworks and Water flowing scenes, and there is even a Baby cry translation (Figure 11, third row (b)) that matches quite precisely both structure and color, except for the baby's clothes. In fact, sound signals carry clues about the surrounding space and scene elements, and this information may be modeled by the translator in order to help picture the output image. Therefore, what seems to happen by chance can be revealing learning processes occurring in multimodal intermediate layers of the translator. Figure 10: Comparison showing a visual decoupling between original and synthetic images among the five sound classes; from top to bottom row: Baby cry, Dog, Rail transport, Fireworks, and Water flowing. S2I translation was performed from unknown sounds using a single translator model conditioned on 512-dimension audio embeddings. The babies’ faces in the images on the left were intentionally pixelated to preserve the children’s identities. _Beyond informativity._ Apart from the informative/non-informative images perspective, we identified the following types of translated images:  
* Defective--Images that may even be informative, but inaccurately depict elements concerning color, luminosity, size, and/or position. * Incomplete--Images that may be informative to a certain degree, but lack some essential part of the element of interest. Or, despite having a coherent surrounding scene, the sound-emitting source is omitted. * Artifactual--Non-informative images that are rather abstract, consisting basically of unrecognizable forms. * Implausible--Images that may occasionally be informative, but that contain awkward or unlikely elements. * Surreal--Images that may present some degree of informativity but have a curious or fantastical appearance. * Creepy--Images that may be partially informative, but that portray parts of living beings in a harrowing way, or that contain ghostly or alien-looking elements. These images could sometimes also be considered defective or surreal, depending on the case. * Multi-informative--These images depict elements from two or more sound classes. _Bad translations._ Regarding badly translated images, such results occur due to different reasons. For instance, certain acoustic events might not have been accurately modeled, leading to unusual or nearly abstract images, as occurred with most dog images. In other instances, the model just mistranslated the input sound, generating, for example, a water-fall scene for a passing train sound, or vice versa. Also, issues related to the dataset itself may impact translation quality.