## 5 Experiments
### Informativity Classifiers  
As an additional contribution, we introduce a solution using classifiers to infer whether the translated images are interpretable and semantically coherent, or, briefly, if they are informative. According to the criteria set for the present study, an image is considered informative when it surpasses a minimum threshold of informativity, ensuring that the depiction of the primary sound-emitting source is recognizable. Assessing the true quality of S2I translation posed an additional difficulty during the experiment. Since class information was available, we initially attempted to useordinary image classifiers to evaluate our translator. The idea was to verify whether the generated image would be classified as the input sound corresponding class. However, this alternative proved ineffective, as the reported classification scores were unrealistically high. As discussed in Section 3, S2I translation is a challenging task, and generated images are mostly non-informative. During the tests, over 50% of generated images were reported to match the original sound class, but upon visual inspection, it became evident that this did not represent the translator's actual performance, even when using the best generator models. Two facts made the evaluation of the S2I translation more difficult, producing biased results as to classification. Firstly, the low percentage of informative images complicated the task, which was to be expected, due to the inherent difficulty of the process. This turned out to be a problem, as the large number of non-informative images had the potential to distort the classification outcomes significantly. For instance, if these non-informative images were randomly classified among the five sound classes, they would considerably inflate the overall effective accuracy. Secondly, inaccuracy resulted from the fact that even informative images were essentially different from real images. They often exhibited less sharpness and less visual diversity compared to the actual images used for training, causing the classifier to perform inaccurately when testing synthetic images. We even tried to analyze the output vector of the softmax function from the visual classifier in an attempt to find a correlation between the class distribution and classification reliability, but no improvement was observed through this approach. After the aforementioned unsuccessful attempts, we proposed to classify images as informative or non-informative in order to provide a quantitative evaluation of the S2I translator's performance. It was necessary to train one informativity classifier for each sound class, since general classifiers performed poorly. Besides, such general classification would not benefit the experiment in any way, given that our goal was uniquely to report the translators' performance as accurately as possible. Thus, we trained five informativity classifiers using both informative and non-informative synthetic images translated from validation sounds. It is important to note that two different visual data sectioning processes were employed here. One groups images into five classes of sound, regardless of whether they are real or synthetic images, while the other categorizes them as informative or non-informative images, serving solely to assess the translator's performance. Considering these data perspectives, we built five balanced datasets comprising a total of 5000 synthetic images selected among the outputs of 17 previously trained S2I translator models. Each dataset for a specific sound class contained 1000 images (800 for training and 200 for testing), evenly distributed between informative and non-informative classes. Since, at that moment, we had not yet trained informativity classifiers, the screening of generator models relied on the reported pixel loss and class matching of translated images, while the final image selection was based on subjective evaluation. The S2I translators performed differently for each sound class.