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  • Title: Singing Ability Assessment: Development and validation of a singing test based on item response theory and a general open-source software environment for singing data.
    Author: Silas S, Müllensiefen D, Kopiez R.
    Journal: Behav Res Methods; 2024 Aug; 56(5):4358-4384. PubMed ID: 37672190.
    Abstract:
    We describe the development of the Singing Ability Assessment (SAA) open-source test environment. The SAA captures and scores different aspects of human singing ability and melodic memory in the context of item response theory. Taking perspectives from both melodic recall and singing accuracy literature, we present results from two online experiments (N = 247; N = 910). On-the-fly audio transcription is produced via a probabilistic algorithm and scored via latent variable approaches. Measures of the ability to sing long notes indicate a three-dimensional principal components analysis solution representing pitch accuracy, pitch volatility and changes in pitch stability (proportion variance explained: 35%; 33%; 32%). For melody singing, a mixed-effects model uses features of melodic structure (e.g., tonality, melody length) to predict overall sung melodic recall performance via a composite score [R2c = .42; R2m = .16]. Additionally, two separate mixed-effects models were constructed to explain performance in singing back melodies in a rhythmic [R2c = .42; R2m = .13] and an arhythmic [R2c = .38; R2m = .11] condition. Results showed that the yielded SAA melodic scores are significantly associated with previously described measures of singing accuracy, the long note singing accuracy measures, demographic variables, and features of participants' hardware setup. Consequently, we release five R packages which facilitate deploying melodic stimuli online and in laboratory contexts, constructing audio production tests, transcribing audio in the R environment, and deploying the test elements and their supporting models. These are published as open-source, easy to access, and flexible to adapt.
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