Detecting audio deepfakes is crucial to ensure authenticity and security, especially in contexts where audio veracity can have critical implications, such as in the legal, security or human rights domains. Various elements, such as complex acoustic backgrounds, enhance the realism of deepfakes; however, their effect on the processes of creation and detection of deepfakes remains under-explored. This study systematically analyses how factors such as the acoustic environment, user type and signal-to-noise ratio influence the quality and detectability of deepfakes. For this study, we use the WELIVE dataset, which contains audio recordings of 14 female victims of gender-based violence in real and uncontrolled environments. The results indicate that the complexity of the acoustic scene affects both the generation and detection of deepfakes: classifiers, particularly the linear SVM, are more effective in complex acoustic environments, suggesting that simpler acoustic environments may facilitate the generation of more realistic deepfakes and, in turn, make it more difficult for classifiers to detect them. These findings underscore the need to develop adaptive models capable of handling diverse acoustic environments, thus improving detection reliability in dynamic and real-world contexts.

In the field of acoustic scene analysis, this paper presents a novel approach to find spatio-temporal latent representations from in-the-wild audio data. By using WE-LIVE, an in-house collected dataset that includes audio recordings in diverse real-world environments together with sparse GPS coordinates, self-annotated emotional and situational labels, we tackle the challenging task of associating each audio segment with its corresponding location as a pretext task, with the final aim of acoustically detecting violent (anomalous) contexts, left as further work. By generating acoustic embeddings and using the self-supervised learning paradigm, we aim to use the model-generated latent space to acoustically characterize the spatio-temporal context. We use YAMNet, an acoustic events classifier trained in AudioSet to temporally locate and identify acoustic events in WE-LIVE. In order to transform the discrete acoustic events into embeddings, we compare the information-retrieval-based TF-IDF algorithm and Node2Vec as an analogy to Natural Language Processing techniques. A VAE is then trained to provide a further adapted latent space. The analysis was carried out by measuring the cosine distance and visualizing data distribution via t-Distributed Stochastic Neighbor Embedding, revealing distinct acoustic scenes. Specifically, we discern variations between indoor and subway environments. Notably, these distinctions emerge within the latent space of the VAE, a stark contrast to the random distribution of data points before encoding. In summary, our research contributes a pioneering approach for extracting spatio-temporal latent representations from in-the-wild audio data.