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- All Subjects: Jazz
- All Subjects: Songs (High voice) with piano
My grandfather, Al Foon Lai, was a paper son. As an adult, I learned that paper sons were members of paper families that may or may not actually exist except on paper; furthermore paper immigration was the way many Chinese entered the United States to get around the Chinese Exclusion Act (1882-1943). Grandfather held legal status, but grandfather's name was fictitious and thus his entry to the United States in 1920 was illegal. Today by some authorities he would be classified as an illegal immigrant. As Grandfather's status as a paper son suggest, Grandfather's credibility as someone with the legal prerogative to reside in the U.S. was a dynamic construct that was negotiated in light of the changing cultural norms encoded in shifting immigration policies. Grandfather constructed his ethos "to do persuasion" in administrative hearings mandated under the Chinese Exclusion Act that produced asymmetrical power relations. By asymmetrical power relations I mean the unequal status between the administrator overseeing the hearing and Lai the immigrant. The unequal status was manifest in the techniques and procedures employed by the administrative body empowered to implement the Chinese Exclusion Act and subsequent laws that affected Chinese immigrants. Combining tools from narrative analysis and feminists rhetorical methods I analyze excerpts from Al Foon Lai's transcripts from three administrative hearings between 1926 and 1965. It finds that Grandfather employed narrative strategies that show the nature of negotiating ethos in asymmetrical power situations and the link between the performance of ethos and the political and social context.
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.
My proposed project is an educational application that will seek to simplify the<br/>process of internalizing the chord symbols most commonly seen by those learning<br/>musical improvisation. The application will operate like a game, encouraging the<br/>user to identify chord tones within time limits and award points for successfully<br/>doing so.