Aftermath

BACKGROUND: The pseudo-random nature of data sonification has rarely been applied to choral music, an exception being the melodies generated from genetic data (Larsen, 2016). More typically found is the simulation of existing compositional styles by statistically-based machine learning algorithms (Papadopoulos et al., 2016). In the field of textual data visualisation, word clouds select the most frequent document words that are not function words (such as "the"), and show their relative importance via font size (Feinberg, 2010). CONTRIBUTION: In this 6-movement work for SSAATB choir, beatboxer, piano, flute and clarinet, I use my own text on the topic of sexual assault. Movements 1-4 express thoughts and feelings at different stages after sexual assault, in styles that vary from pop song lyrics, to prose statements. Movement 5 (World Cloud) uses a list of frequent words that occurred in a set of documents about sexual assault, sorted in frequency order. Word frequencies were converted to audio frequencies in the choir vocal range. The final movement takes the sentiment of the People v. Turner victim statement's final paragraph, to create an anthem. SIGNIFICANCE:This work is important because it combines a contemporary text on a difficult topic (sexual assault) with a range of modern musical styles ranging from looper-inspired structures to innovative algorithmic composition and data sonification. The work was ranked first and fifth out of fourteen compositions by the expert judging panel of the national ROCS Occasional Choral Composition Competition (I was however ineligible to win as the conductor of the concert).