Filtering by
- All Subjects: Music
- Creators: Barrett, The Honors College
- Creators: Schuring, Martin
Arguably the most noteworthy result was its flourishing musical community. Composers and performers who had worked together in Prague prior to the war were able to continue to do so freely in ways that Jewish people were not allowed anywhere else in occupied Europe. They kept the musicians in Theresienstadt—delaying their deportations to Auschwitz—longer than almost anyone else in the camp, until the threat of Soviet liberation was imminent. This thesis aims to explore the lives and works of four Theresienstadt composers: Viktor Ullmann, Pavel Haas, Gideon Klein, and Hans Krása. All four of these artists were successful prior to the war, spent time in Theresienstadt, and were sent to Auschwitz on the same transport on October 16, 1944. Three of the four died in the gas chambers of Auschwitz, and Klein was sent on to the Fürstengrube concentration camp, where he was shot and killed in January 1945. These composers and their music should be remembered, studied, and performed, not only for historical and moral reasons, but also for artistic ones. Their works represent some of the finest music in the German tradition written during this period. In conjunction with this paper, I have arranged Gideon Klein’s String Trio—one of the pieces profiled here—for saxophone quartet. Members of the Arizona State University saxophone studio will perform it twice in April. I hope that the performances will help make audiences aware of the strength of the music that came out of Theresienstadt, and reinforce the fact that it remains highly relevant. In this thesis, the composers’ careers before and during their time in Theresienstadt will be traced, as well as the measures they took to preserve their music, their interactions with each other, and their efforts to use hidden messages in their music. It is hoped that this document will help fill an important gap in the history of European music in the twentieth century.
The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.