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- All Subjects: Violin and piano music
Unfortunately there is virtually no existing scholarship on Prince and even basic information regarding his life and works is not readily available. The lack of organization of the manuscript scores and the absence of dates of his works has further pushed the composer into obscurity. An investigation therefore was necessary in order to explore the neglected aspects of the life and works of Prince as a violinist and composer. This document is the result of such an investigation by including extensive new biographical information, as well as the first musical analysis and edition of the complete recovered works for violin and piano.
In order to fill the gaps present in the limited biographical information regarding Prince’s life, investigative research was conducted in Mexico City. Information was drawn from archives of the composer’s grandchildren, the Palacio de Bellas Artes, the Conservatorio Nacional de Música de México, and the Orquesta Sinfónica Nacional. The surviving relatives provided first-hand details on events in the composer’s life; one also offered the researcher access to their personal archive including, important life documents, photographs, programs from concert performances, and manuscript scores of the compositions. Establishing connections with the relatives also led the researcher to examining the violins owned and used by the late violinist/composer.
This oral history approach led to new and updated information, including the revival of previously unpublished music for violin and piano. These works are here compiled in an edition that will give students, teachers, and music-lovers access to this unknown repertoire. Finally, this research seeks to promote the beauty and nuances of Mexican salon music, and the complete works for violin and piano of Samuel Máynez Prince in particular.
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.