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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.
Music streaming services have affected the music industry from both a financial and legal standpoint. Their current business model affects stakeholders such as artists, users, and investors. These services have been scrutinized recently for their imperfect royalty distribution model. Covid-19 has made these discussions even more relevant as touring income has come to a halt for musicians and the live entertainment industry. <br/>Under the current per-stream model, it is becoming exceedingly hard for artists to make a living off of streams. This forces artists to tour heavily as well as cut corners to create what is essentially “disposable art”. Rapidly releasing multiple projects a year has become the norm for many modern artists. This paper will examine the licensing framework, royalty payout issues, and propose a solution.