## 1 Introduction  
In the last few decades, acoustic event detection (AED) has evolved from adopting techniques initially developed for automatic speech recognition [1] to the use of deep learning (DL) models [2]. However, most current AED systems still rely on classification processes, and from a human receiver's standpoint, the model output has not changed much. In such systems, inference results are limited to discrete labels that represent sound concepts. A class-based output might suit an automatic audio monitoring system within a restricted acoustic context. Nonetheless, considering the intricate nature of environmental sounds, which encompass a vast and diverse spectrum of concepts, such output can easily mislead the human receiver, inducing a poor interpretation of the actual sound scene. In a previous study [3], deaf participants tested a mobile sound-recognition system and expressed their preference for images rather than text to represent sounds in the application. In the present work, as an alternative to sound classification, we explore the possibilities of automatic sound-to-image (S2I) translation for visually conveying the occurrence of acoustic events. We propose a system (Figure 1) that, given an audio input, is able to 'imagine' the scene with the sound-emitting source, generating an original image based on knowledge acquired through audiovisual learning. Furthermore, the system is expected to generate images that are interpretable and semantically coherent with the corresponding acoustic event of the captured audio. Throughout this text, we will occasionally refer to these images as informative. Here, the use of this term is aligned with the concept of 'informativity' in the context of text translation, as described by [PERSON] and [PERSON] [4]:\"Informativity in the translation process is a measure of the information a translation provides to an L2 reader about L1 events, states, processes, objects, individuals, places and institutions. The original information source was an L1 text intended for L1 audience. Translation opens an information channel between senders and receivers who could not normally inform one another about their respective states of affairs.\" L1 and L2 mean, respectively, source language and target language. Analogously, within the context of S2I translation, we define 'informativity' as the measure of information transmitted from the aural to the visual modality about sound-related events and sound-emitting sources, as well as any picturable elements in the surroundings that can be inferred from the sound. This might include landscapes, environments, people, backgrounds, surfaces, and objects. From pioneering works that employed data mining techniques to match words and image parts [5], to recent approaches based on DL models, cross-modal initiatives have gained the perspectives of new horizons. These proposals all share a fundamental strategy: the creation of a bridge that connects different modalities. DL models enable efficient data processing, since they can achieve higher abstraction levels through automatically learned features. Additionally, in the case of convolutional neural networks (CNNs) employed for computer vision, interpretable features may be generated in their inner layers. According to [PERSON] et al. [6], semantic parts and objects emerge spontaneously with CNNs trained for scene classification. [PERSON] et al. [7] verified that roughly 10 to 20% of these inner features can represent interpretable concepts tied to textures, materials, semantic parts, and objects. Furthermore, [PERSON] et al. [8] observed that images and sounds have complementary information about the occurrence of common events on a video stream. Those findings reveal a potential strategy for building the cross-modal bridge, one which is based on the assumption that both visual and aural modalities share extractable semantic information about acoustic events. For example, a video of a beach scene might contain both the imagery and sounds of waves crashing on the shore.