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“Good composers borrow, Great ones steal!”

Emelie Curl, Tong Shan, Danielle Brager, Glenn Young, Larsen Linov, Reginald Bain


This is one of the four projects I suggested during pitch day. I have chosen one of them.
Musical borrowing has a long history, spanning "classical" music all the way up to the music we would consider “modern.” Some examples include:
- The famed Baroque period composer and musician, Johann Sebastian Bach, borrowed music from his predecessors, contemporaries, and even himself. He notably "borrowed" many of the concertos of Vivaldi.
- The prolific and influential composer of the Classical period, Wolfgang Amadeus Mozart, debuted his Symphony No. 37 in G major in 1783, but it was mostly copied from Haydn’s Symphony No. 25 in G major.
- Brian Wilson of the Beach Boys was listed as the sole writer of "Surfin' USA" when it was released in 1963. However, it was discovered that the Beach Boys' "Surfin' USA" was lifted directly from Chuck Berry's "Sweet Little Sixteen," so Chuck Berry was later given writing credit and publishing royalties.
- Finally, consider the court case of Marvin Gaye's heirs vs. Robin Thicke & Pharrell Williams. Robin Thicke and Pharrell Williams were sued for plagiarizing Gaye's "Got to Give it Up" in their song, "Blurred Lines." In 2015, Gaye's heirs won the $7.4 million case, though it was lowered to $5.3 million afterwards.
Questions that could be asked:
- How can we use deep learning to determine which classical pieces or modern songs have been “borrowed” the most?
- How can we used deep learning to determine what classical composer or modern artist is guilty of “borrowing” the most?
- How can we use deep learning to determine what famous composers or modern musicians “sound” the most like each other?
- Using deep learning to examine some of famous court cases that have arisen in accordance with accusations of music plagiarism to answer some of these questions is an option.
- Also, open to other questions as well.
There are various similar projects to use as sources and an abundance of data sets to work with.

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