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Title MUSIB: Musical Score Inpainting Benchmark
Authors Mauricio Araneda, Felipe Bravo-Marquez, Denis Parra, Rodrigo Cádiz
Publication date April 2023
Abstract Music inpainting is a sub-task of automated music
generation that
aims to infill incomplete musical pieces to help musicians in their musical
composition process. Many methods have been developed for this task.
However, we observe a tendency for each method to be evaluated using
different datasets and metrics in the papers where they are presented. This
lack of standardization hinders an adequate comparison of these approaches.
To tackle these problems, we present MUSIB, a new benchmark for musical
score inpainting with standardized conditions for evaluation and
reproducibility. MUSIB evaluates four models: Variable Length Piano
Infilling (VLI), Music InpaintNet, Music SketchNet, and AnticipationRNN, and
over two commonly used datasets: JSB Chorales and IrishFolkSong. We also
compile, extend, and propose metrics to adequately quantify note attributes
such as pitch and rhythm with Note Metrics, but also higher-level musical
properties with the introduction of Divergence Metrics, which operate by
comparing the distance between distributions of musical features. Our
evaluation shows that VLI, a model based on Transformer architecture, is the
best performer on a larger dataset, while VAE-based models surpass this
Transformer-based model on a relatively small dataset. With MUSIB, we aim at
inspiring the community towards better reproducibility in music generation
research, setting an example for strongly founded comparisons among SOTA
Pages article 19
Volume 2023
Journal name EURASIP Journal on Audio, Speech, and Music Processing
Publisher Springer (Netherlands)
Reference URL View reference page