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Abstract
The DALI dataset is a large dataset of time-aligned symbolic vocal melody notations (notes) and lyrics at four levels of granularity. DALI contains 5358 songs in its first version and 7756 for the second one. In this article, we present the dataset, explain the developed tools to work the data and detail the approach used to build it. Our method is motivated by active learning and the teacher-student paradigm. We establish a loop whereby dataset creation and model learning interact, benefiting each other. We progressively improve our model using the collected data. At the same time, we correct and enhance the collected data every time we update the model. This process creates an improved DALI dataset after each iteration. Finally, we outline the errors still present in the dataset and propose solutions to global issues. We believe that DALI can encourage other researchers to explore the interaction between model learning and dataset creation, rather than regarding them as independent tasks.
Highlights
The singing voice is one of the most important elements in popular music
The singing voice defines the central melody around which songs are composed and adds a linguistic dimension that complements the abstraction of musical instruments
The main motivation to improve pis that sing at the same time or when a chorus is repeated at the end but not its lyrics
Summary
The singing voice is one of the most important elements in popular music. It combines two primary music dimensions: melody and lyrics. Together, they tell stories and convey emotions enriching our listening experience. The singing voice is a lesserstudied topic in the Music Information Retrieval (MIR) community. The lack of large and good quality datasets is one of the main issues when working on singing voice related tasks. It prevents the MIR community from training state-of-the-art machine learning (ML) algorithms and comparing their results
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