## 2 Related Work
The authors showcase the outcomes of their S2I translation for two sound classes, namely, Baseball and Soccer, obtaining informative images. However, the dataset utilized for this experiment consists of only 2065 sound-image training pairs. Furthermore, a notable limitation present in both studies is the data-splitting method, which does not prevent sound-image pairs of the same video from appearing in both training and test sets. This procedure compromises the evaluation of the system, since the model may be tested with sounds from known scenes. Another S2I task that has already been explored involves generating images of faces from speech audio. [PERSON] et al. [22] present an end-to-end solution, named Wav2 Pix, aimed at addressing this cross-modal challenge. They employ a GAN conditioned on an audio embedding extracted from speech. The model is capable of generating realistic and diverse facial images. However, their method requires the use of a clean dataset with precisely framed faces and high-quality audiovisual content. Additionally, to obtain acceptable results, their generator needs to be conditioned on known voices. [PERSON] et al. [23] present a model called SpeechFace which tackles a similar problem. They train an encoder to align visual embeddings with those generated by a pre-trained face-recognition network [24]. The subsequent decoding process is performed using a separately trained reconstruction model [25], generating images of faces in a canonical form--precisely framed, frontally positioned, and displaying a neutral expression. Unlike our approach, these works address a specific domain, which significantly reduces audiovisual content diversity. [PERSON] and [PERSON] [26] present the Sound2 Sight framework, designed for generating video frames conditioned on the audio track and preceding frames. Briefly described, their framework follows an encoder-decoder auto-regressive generator architecture, one which produces one video frame at a time using two long short-term memory (LSTM) networks. Different from our approach, Sound2 Sight does not process pure S2I translation, as the visual modality is also present in the input. This aspect enhances the generation of plausible images, but characterizes the process as a multimodal task rather than a cross-modal one. Moreover, the conducted experiment utilizes three datasets separately, each consisting of specific audiovisual content, resulting in limited diversity. Also addressing a S2I task, [PERSON] et al. [27] propose an end-to-end solution for generating images of birds conditioned on call sounds of correspondent species. After training a sound classifier, they obtain the audio embedding, which then serves as input to a conditional GAN. Unlike our proposal, they employ a class-based encoder. Additionally, their adversarial training is also supervised, as their discriminator is trained not only to assess the realness of generated images but also to predict the species label. In another study on bird sounds, [PERSON] et al. [28] present AEMCGAN, a framework based on their previous work [17], and designed to perform both I2S and S2I translation with the addition of at tention modules to capture intra- and inter-modality global dependencies. They obtained improved results compared to prior studies by using their own dataset for cross-modal translation of bird sounds and a subset of the previously mentioned URMP dataset. However, both datasets are domain-specific, focusing on a limited range of audiovisual diversity. Another drawback of these two studies is that the split of the data does not prevent the model from being tested with sounds from known audio streams. As previously mentioned, this procedure compromises the evaluation of translation quality. [PERSON] et al. [29] conducted a study on multi-modal image generation for audiovisual inpainting. Yet, their model is also capable of performing cross-modal generation conditioned on sound only. Their pipeline consists of a coarse-to-fine two-staged process.