September 25, 2017

A Dataset and Baseline System for Singing Voice Assessment

Barış Bozkurt, Ozan Baysal and Deniz Yuret. 2017. In The 13th International Symposium on Computer Music Multidisciplinary Research (CMMR), September. (PDF)

Abstract: In this paper we present a database of fundamental frequency series for singing performances to facilitate comparative analysis of algorithms developed for singing assessment. A large number of recordings have been collected during conservatory entrance exams which involves candidates’ reproduction of melodies (after listening to the target melody played on the piano) apart from some other rhythm and individual pitch perception related tasks. Leaving out the samples where jury members’ grades did not all agree, we deduced a collection of 1018 singing and 2599 piano performances as instances of 40 distinct melodies. A state of the art fundamental frequency (f0) detection algorithm is used to deduce f0 time-series for each of these recordings to form the dataset. The dataset is shared to support research in singing assessment. Together with the dataset, we provide a flexible singing assessment system that can serve as a baseline for comparison of assessment algorithms.

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