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comparatively small repertoire. Until the mid-20th century, composers did not view the
bass clarinet as a solo instrument and instead perceived it as cumbersome due to its low
pitch and predominant use as an accompaniment instrument, resulting in a dearth of solo
repertory for the bass clarinet before this time. Bass clarinetists desiring to perform
repertoire from the Baroque, Classical, and Romantic periods must then appropriate
music from other instruments. Through this study, I identify and detail a process for
creating informed transcriptions of music for the bass clarinet to increase its body of solo
and chamber literature. I examine the original scores and existing transcriptions of
Concerto in C minor by Henri Casadesus (attributed to Johann Christian Bach) for cello,
Bassoon Concerto Op. 75 by Carl Maria von Weber, Trios, Hob. IV:1-4 “London Trios”
by Joseph Haydn, Kol Nidrei, Op. 47 by Max Bruch, and Clarinet Concerto in A Major,
K. 622 by Wolfgang Amadeus Mozart to identify methods for the transcription process. I
compare this to the transcription process for other instruments through examination of the
Clarinet Sonatas, Op. 120, Nos. 1 and 2 by Johannes Brahms, which were transcribed
from clarinet to viola by the composer himself. In this document, I discuss the historical
background of the selected pieces, the selection process, editing considerations,
performance practice, and the usage of transcriptions as a pedagogical tool. Although
transcriptions for the bass clarinet already exist, appropriation of music from other
instruments will continue to supplement and diversify its repertoire. These pieces serve to
develop important technical and musical skills and allow the bass clarinetist to play
music across various style periods. In this project, I select and transcribe three pieces for
the bass clarinet: Sonata for Cello No. 1 in F Major by Benedetto Marcello, Grand
Concerto for Bassoon and Orchestra by Johann Nepomuk Hummel, and Serenade in F
minor, Op. 73, by Robert Kahn. The transcribed scores are included in the appendices of
this document.
Standardization is sorely lacking in the field of musical machine learning. This thesis project endeavors to contribute to this standardization by training three machine learning models on the same dataset and comparing them using the same metrics. The music-specific metrics utilized provide more relevant information for diagnosing the shortcomings of each model.
In this paper, I propose that taking an embodied approach to music performance can allow for better gestural control over the live sound produced and greater connection between the performer and their audience. I examine the many possibilities of live electronic manipulation of the voice such as those employed by past and current vocalists who specialize in live electronic sound manipulation and improvisation. Through extensive research and instrument design, I have sought to produce something that will benefit me in my performances as a vocalist and help me step out from the boundaries of traditional music performance. I will discuss the techniques used for the creation of my gestural instrument through the lens of my experiences as a performer using these tools. I believe that, through use of movement and gesture in the creation and control of sound, it is more than possible to step away from conventional ideas of live vocal performance and create something new and unique, especially through the inclusion of improvisation.