Dissecting Temporal Understanding in Text-to-Audio Retrieval

In this work, we build on [34] and examine the limitations of current state-of-the-art text-audio models, particularly in their Recent advancements in machine learning have fueled research use of temporal information. Different from [34] that considers on multimodal tasks, such as for instance text-to-video and textto-audio a text-audio model containing a CNN-based audio encoder, our retrieval. These tasks require models to understand the analysis uses the recent transformer-based audio encoder HTS-semantic content of video and audio data, including objects, and AT [4] that serves as a component of state-of-the-art text-to-audio characters. The models also need to learn spatial arrangements and retrieval models [24, 35]. We assess whether the model containing temporal relationships. In this work, we analyse the temporal ordering a transformer-based audio encoder results in better temporal of sounds, which is an understudied problem in the context of understanding abilities than a CNN-based one.