Michael- Finished an algorithm to parse midi files to format useable by machine learning algorithm. It currently only works with type-0 midi files which is one track. This is not a bad assumption for our use case for now since we are focused on a melody and not multiple instruments at once. However other type midi files can typically be transposed in the future if need be. Used the mido library for the current algorithm. However was having some difficulties with inconsistencies between midi files and looked into mingus and LilyPond library to convert to a sheet music object and then parse. Ended up being able to figure out the parsing with mido so this is not needed at the time. For next week I want to work on the output algorithm going from machine learning output back to midi file and then incorporating with webapp.
Chris – This week has been a really busy one for me, just spent another weekend in New York for an interview; please pardon me for my lateness in making this post. My main task for the past week, and for the coming week as well is to figure out how to algorithmically decide the key, or in another word, the scale of a melody. This is crucial because the assumption we made for our main machine learning model is that all input melodies are already transposed to Cmaj key, which requires us to find the key of the melody in the first place. Although some MIDI file comes with the key in the metadata, we cannot guarantee that it is the case for all customized user inputs. I spent the majority of the time the past week researching the background of music key determinization, and it turns out to be much more complicated than originally expected. There are two popular approaches, the first one being the Krumhansl-Schmuckler key-finding algorithm (http://rnhart.net/articles/key-finding/), which is a statistical approach, and the other being a machine learning approach (https://arxiv.org/pdf/1706.02921.pdf). I have been researching both methods and evaluating which one is suitable for our project in terms of time, scale, and feasibility.
Aayush – got the lstm to predict chords, they are fairly reasonable from a music theoretic viewpoint, i.e. the notes in each bar generally correspond to the notes of the chord. Very limited testing has been done though, will now work on evaluation and testing different features/output prediction formats. Will then move onto dataset curation and hyperparameter tuning subject to results