## 2 Related Work  
The present study proposes a S2I translation system employing DL models [10] to generate content that is both perceptually meaningful and semantically coherent for human receivers. In this section, we provide a concise literature review of studies that have employed DL methods for aural-visual cross-modal processes. [PERSON] et al. [11] conducted a study on S2I translation using conditional GANs [12]. While adopting a translation structure similar to the one used by [PERSON] et al. [13] and [PERSON] et al. [14], they addressed a different problem: cross-modal content generation in both directions, image-to-sound (I2S) and S2I. Their aim was to translate audio tracks of musical solo performances into images of a person playing the corresponding instrument, and vice versa. Following the strategy of previously mentioned cross-modal approaches, and drawing from the DCGAN architecture [15], their system consisted of an encoder and a conditioned GAN. In the context of S2I translation, their model produced good results when tested on the URMP audiovisual dataset [16] comprised of studio-quality video tracks featuring uniformly framed individuals playing instruments against a blue background. However, when tested on a more diverse dataset, the quality of the synthesized images dropped considerably. [PERSON] et al. [17] presented a framework called CMCGAN to perform cross-modal aural-visual mutual generation. While also capable of performing I2S and S2I translation using the URMP dataset, their framework demonstrated an enhancement in quality for cross-modal reverse translation from synthetic images/sounds compared to the same task using ground-truth image/sound pairs. This progress was attributed to their improved handling of dimension and structural asymmetry across different modalities, which was achieved through noise injection. Similar to the work by [PERSON] et al. [11], the weak points of this approach are its low diversity and the uniformity of the audiovisual content. [PERSON] et al. [18] utilized the same URMP dataset to carry out a cascade coarse-to-fine S2I translation. Instead of feature embeddings, they opted for a supervised approach to keep the cross-modal translation consistent with high-level semantics. Employing attention mechanism on generators, in addition to class-based loss across all learning stages, and a residual class label to guide finer image generation, they were able to improve significantly the results obtained by [PERSON] et al. [11] and [PERSON] et al. [17]. Different from these approaches, our work proposes addressing S2I translation without employing any type of supervision for training, i.e., avoiding class-based losses, and utilizing larger and more diverse audiovisual datasets. [PERSON] et al. [19] present the outcomes of their S2I translation proposal, one which uses conditional GANs trained on video data. Unlike ours, their approach is entirely supervised. Apart from extracting the audio feature vector from SoundNet [20], their generator and discriminator are both trained with an auxiliary classifier to enhance the semantic coherence between the generated image and the corresponding input sound. Also, they propose a sound-image similarity score to improve discriminator training. Similar to the strategy employed by [PERSON] et al. [11], their S2I translator generates relatively informative images when applied to audiovisual data characterized by low content diversity and uniform backgrounds. The sound classes used are: Baseball, Dam, Plane, Soccer, and Speedboat. Utilizing a subset of the same dataset, [PERSON] et al. [21] present an S2I translator based on a stacked GAN architecture. Like the work of [PERSON] et al. [19], their approach is entirely supervised. They also use SoundNet-extracted audio features for the generator input and an auxiliary classifier for GANs training. Moreover, they reverse the translation via an I2S net to validate the audio-content consistency between the original audio embedding and the reversed one.