This week we worked collectively on researching Machine Learning algorithms, devising strategies to acquire data, scheduling, and planning out certain design decisions of our project.
Aayush – I collected a dataset from Wikifonia.org, which is now a defunct website, but their data is still available in places online. I also researched on approaches to chord generation (https://arxiv.org/pdf/1712.01011.pdf). For the next week I plan to study more of the background described in music generation papers (HMM’s, Neural Networks).
Michael – I also researched chord progression and other related music generation papers using Machine Learning https://cs224d.stanford.edu/reports/allenh.pdf https://arxiv.org/pdf/1712.01011.pdf https://www.researchgate.net/publication/319524552_Deep_Learning_Techniques_for_Music_Generation_-_A_Survey
Chris- For the past week my main focus was to collect data, determine the usability of the dataset and to research on related fields. I researched existing tools to convert chord labels in music files into notes as many data we found has only the main melody well labeled while the chord progressions are labeled in plain text format. Useful tool: MuseScore, and useful plugin. For the first half of the next week, I will move onto researching algorithms for chord generation (BTSTM, Bidirectional Long Short Term Memory). For the later half of the week, I will work with my teammates to pre-process the music data if needed.
Our short term goal over the next few weeks is to run short tests (2-4 bars) that will help us scope out the task for the design review.