Andrés Santiago Cisneros-Barahona, Luis Marqués-Molías, Nicolay Samaniego-Erazo, Catalina Mejía-Granizo, Gabriela De la Cruz-Fernández
Multivariate data analysis: Validation of an instrument for the evaluation of teaching digital competence
F1000Research
musical instrument classification task wellstudied remains gap identifying nonpitched percussion instruments greater overlaps frequency bands variation sound quality play style pitched instruments paper present musical instrument classifier detecting tambourines maracas castanets instruments often used early childhood music education generated dataset diverse instruments eg brand materials construction played different locations varying background noise play styles conducted sensitivity analyses optimize feature selection windowing time model selection deployed evaluated best model mixed reality music application 12 families home setting dataset comprised 369000 samples recorded inlab 35361 samples recorded families home setting observed light gradient boosting machine lgbm model perform best using approximate 93 ms window 12 melfrequency cepstral coefficients mfccs signal entropy best lgbm model observed perform 84 accuracy across three instrument families inlab 73 accuracy deployed home knowledge dataset compiled 369000 samples nonpitched instruments first kind work also suggests low feature space sufficient recognition nonpitched instruments lastly realworld deployment testing algorithms created participants diverse physical cognitive abilities also important contribution towards inclusive design practices paper lays technological groundwork mixed reality music application detect childrens use nonpitched percussion instruments support early childhood music education play