Predicting Music Hierarchies with a Graph-Based Neural Decoder

Autor:	Foscarin, Francesco, Harasim, Daniel, Widmer, Gerhard
Rok vydání:	2023
Předmět:	Computer Science - Sound Computer Science - Computation and Language Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	This paper describes a data-driven framework to parse musical sequences into dependency trees, which are hierarchical structures used in music cognition research and music analysis. The parsing involves two steps. First, the input sequence is passed through a transformer encoder to enrich it with contextual information. Then, a classifier filters the graph of all possible dependency arcs to produce the dependency tree. One major benefit of this system is that it can be easily integrated into modern deep-learning pipelines. Moreover, since it does not rely on any particular symbolic grammar, it can consider multiple musical features simultaneously, make use of sequential context information, and produce partial results for noisy inputs. We test our approach on two datasets of musical trees -- time-span trees of monophonic note sequences and harmonic trees of jazz chord sequences -- and show that our approach outperforms previous methods. Comment: To be published in the Proceedings of the International Society for Music Information Retrieval Conference (ISMIR)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.16955 Zobrazit plný text záznamu View this record from Arxiv