Michael – Continued to work on programming the web application. Can successfully load and play a midi file. Still working on being able to send the midi file from javascript to python. My goal for the coming week is to work on the design documents firstly and make sure I understand all the libraries need for processing the midi files.
Chris – For the past week I took a deeper dive into the machine learning algorithm. I was mainly looking at the BLSTM and HMM. Additionally, I looked into how to use Keras, a Python machine learning library to set up the BLSTM algorithm. For the coming week, my goal is to work with Aayush to get the machine learning algorithm going to a point that we can feed the test music data in and get some reasonable output. On the side, for the past week, I also spent some time looking at some music theory documents to understand the mechanism behind how chords and melody work together. This way when we have the test ready and going, I will be able to evaluate, music-theory-wise, of the quality of our chord outputs, instead of just listening to them and judging by ear.
Aayush – I have been working on machine learning tests. I found a model here (https://github.com/luggroo/230-melody-to-chords) that used lstm’s and Gated Recurrent Units (GRU’s) for chord generation, however when we reproduced their results, they was quite poor, mostly due to lack of data. With the help of this code however, I was able to identify changes we would need to make to process the wikifonia dataset, and to implement the design proposed in the presentation in Keras. This includes adding the bidirectional layer (well supported in keras) and zero padding to handle songs of varying length. Working this week on the design presentation, followed by implementing the model in https://arxiv.org/pdf/1712.01011.pdf using the wikifonia dataset.