This paper studies the capability of an object-centric audio representation to support multiple downstream tasks within a unified framework. The object-centric model is trained in a self-supervised setup using slot attention by reconstructing Audio Spectrogram Transformer (AST) patch embeddings from a set of latent slots. It is trained on synthetic audio mixtures, which provides controlled multi-source audio scenes. These slot representations are evaluated for the classification, detection, and separation of sound events by training task-specific output layers. On the classification task, the model achieves an F1-score of 0.8, comparable to clip-level AST embeddings which achieve 0.7. For sound event detection, it reaches a macro F1-score of 0.7, outperforming clip-level AST embeddings at 0.4 and matching AST patch embeddings at 0.7, which encode finer time–frequency information. In source separation, it achieves an SI-SDR improvement (SI-SDRi) of 18.4 dB. Furthermore, in a zero-shot source separation setting, the model achieves an SI-SDRi of 14.5 dB. These results demonstrate that slot-based representations capture transferable and compositional structure in audio scenes, enabling generalization across tasks and datasets.