TempEst: Harpsichord Temperament Estimation in a Semantic Web Environment
Dan Tidhar, György Fazekas, Matthias Mauch, Simon Dixon · Journal of New Music Research · 2010
Issues concerning tuning and temperament bear relevance to music research in areas such as historical musicology, performance and recording studies, and music perception. We have recently demonstrated that it is possible to classify keyboard temperament automatically from audio recordings of standard musical works to the extent of accurately distinguishing between six different temperaments often used in harpsichord recordings. The current paper extends this work by combining digital signal processing with semantic computing and demonstrates the use of the temperament classifier in a Semantic Web environment. We present the Temperament Ontology which models the main concepts, relationships, and parameters of musical temperament, and facilitates the description and inference of various characteristics of specific temperaments. We then describe TempEst, a web application for temperament estimation. TempEst integrates the classifier with ontology-based information processing in order to provide an extensible online service, which reports the class and properties of both known and unknown temperaments. TempEst allows users to upload harpsichord recordings, and provides them with an estimated temperament as well as other inferred characteristics of the instrument's tuning.