Dave Final Project – Fish in a Pond
A fish which can learn what kind of melody the user likes via machine learning and plays them.
I was inspired by my research professor’s project “Simstudent”, in which a human student walks a computer Simstudent through the steps of algebra problems, from which the Simstudent will learn via machine learning. While I was testing it, I found I greatly enjoyed teaching and watching my Simstudent succeed on the problems that once baffled it. Thus, I started off looking for ways to use machine learning as the backend of my project. Music seemed like a good idea, so I went for it, despite having zero experience.
I used Weka’s implementation of the ADTree algorithm as my backbone. I represented a melody as an array of 10 notes, which is limited to 7 pitches, as recommended by Professor Richard Randall. The user can either rate a melody played by either the fish or the user as favorable or unfavorable, from which the fish learns. My thoughts on what the frontend looks like revolved around a fish, because they don’t seem like creatures that will likely be playing music, so I implemented it as such.
In hindsight, music is probably not the best thing for me to do;. I do not have the skills to hear the musical structures in the melodies that were created, and along with the fact that whether melodies are good or bad to a user is highly subjective, caused me to be unable to statistically confirm whether the algorithm is robust. I did however have a musically gifted friend play around with it, and after around 15 trials, he claimed that the fish picked up on a structure that he had played. I also managed to train my fish to know that it must play the last note at a low pitch, which if that means good melodies in my heart, then I am successful.
Source code can be found in this repository: