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Music is characterized by complex hierarchical structures. Developing acomprehensive model to capture these structures has been a significantchallenge in the field of Music Information Retrieval (MIR). Prior research hasmainly focused on addressing individual tasks for specific hierarchical levels,rather than providing a unified approach. In this paper, we introduce aversatile, all-in-one model that jointly performs beat and downbeat tracking aswell as functional structure segmentation and labeling. The model leveragessource-separated spectrograms as inputs and employs dilated neighborhoodattentions to capture temporal long-term dependencies, along with non-dilatedattentions for local instrumental dependencies. Consequently, the proposedmodel achieves state-of-the-art performance in all four tasks on the HarmonixSet while maintaining a relatively lower number of parameters compared torecent state-of-the-art models. Furthermore, our ablation study demonstratesthat the concurrent learning of beats, downbeats, and segments can lead toenhanced performance, with each task mutually benefiting from the others.

All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio