Sandweiler, Luxembourg, Luxembourg
Torsten Anders is a composer, researching and developing software for computer-aided composition with 20+ years of experience in this field. Torsten Anders studied composition at the Franz Liszt Academy of Music in Weimar (1994-2000, MA in 2000). He studied instrumental composition with Wolfgang von Schweinitz and Michael Obst, as well as electro-acoustic composition with Hans Tutschku, and Robin Minard, among others. Between 2000-2001, Anders worked as a computer programmer in the Music, Mind, Machine research group at the University of Nijmegen (NL). Anders received a PhD in Music (Music Technology) from Queen's University Belfast (UK) in 2007, where he was a member of the Sonic Arts Research Centre. He served as a research fellow at the Interdisciplinary Centre for Computer Music Research, University of Plymouth (UK) in 2007-2010. Anders worked as Senior Lecturer and course leader for music technology at the University of Bedfordshire until 2019, when he joined the Aiva team. Anders' theoretical research focuses on the computational modelling of composition and music theories. He developed the constraint-based composition system Strasheela and various other software. Anders composes instrumental music, multichannel tape pieces and sound installations, which have been performed in several European countries, in America, and the Far East.
Developed various backend features of an AI-based algorithmic composition system. E.g., shaped the global form of music; added expressive performance; data crawling and pre-processing for training; and prompt engineering.
Course Leader of the degree BA(Hons) Music Technology. Responsible for the design, delivery and management of this course. Research in algorithmic composition.
Design and implementation of compositional applications that make use of brain imaging data (EEG, fMRI) and related research; Continued development of the constraint-based composition system Strasheela.
Design and implementation of several music software projects (e.g. automatic score transformation for music notation, visualisation of hierarchic musical structures, processing of the result data from rhythm perception experiments into an internet presentation).