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

Taejun Kim; Juhan Nam

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arXiv · 2307.16425

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

Taejun Kim·Juhan Nam

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Abstract

Music is characterized by complex hierarchical structures. Developing a comprehensive model to capture these structures has been a significant challenge in the field of Music Information Retrieval (MIR). Prior research has mainly focused on addressing individual tasks for specific hierarchical levels, rather than providing a unified approach. In this paper, we introduce a versatile, all-in-one model that jointly performs beat and downbeat tracking as well as functional structure segmentation and labeling. The model leverages source-separated spectrograms as inputs and employs dilated neighborhood attentions to capture temporal long-term dependencies, along with non-dilated attentions for local instrumental dependencies. Consequently, the proposed model achieves state-of-the-art performance in all four tasks on the Harmonix Set while maintaining a relatively lower number of parameters compared to recent state-of-the-art models. Furthermore, our ablation study demonstrates that the concurrent learning of beats, downbeats, and segments can lead to enhanced performance, with each task mutually benefiting from the others.

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Taejun Kim, Juhan Nam. 2023-07-31. All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio. https://arxiv.org/abs/2307.16425

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