CLASSICAL COMPOSER: SONG GENERATION USING TUNE PATTERNS JESSICA OUYANG, KATE RYDBERG

INTRODUCTION RESULTS IN PROGRESS Representative generated song sampling by method: Adaptation of Model Making songs and tunes is a task that typically requires TUNE 1: Window Length of 4, Key of C Major, MSD Complexity abundant human input: a composer, producer, knowledge • Include consideration of music theory. We wanted to create an algorithm that of several other factors could produce a believably human-made tunes based on (harmony/background existing song patterns. Listen to the music, music genre, TUNE 2: Window Length of 4, MSD, Length consideration Song Samples segment generation) METHOD RNN to Generate Music 1. Processing Data • Features include time • Strongest pitch at given time segment assessed signature, key, previous • 2D Vector of segment pitch data per Million Song TUNE 3: Window Length of 3, Beethoven Dataset, Length consideration notes Dataset song • Considers a larger • Classical Piano Midi Dataset processed by pitch window of notes to allow value with Music21 to constrain genre for more cohesive music • Current note maps to array of all successive notes to patterning throughout capture successive probability while allowing for variability piece • Varying lengths of window sizes assessed with song TUNE 4: Window Length of 5, Beethoven Dataset, Length consideration shape/pattern vs. variation tradeoff • Notes separated by key of song when available FUTURE WORK • Same note in two successive time segments later interpreted as single note We hope to develop a Generative Adversarial Network in order to allow for learning and better understand how ANALYSIS the songs generated fit • N-grams was able to generate some common progressions in music, into the context of Sample Segment Pitches 2D Value Vector but was not good at creating patterns over time throughout the song believability. • Length consideration allowed for more structure based on observed music and natural-sounding variation • Midi file dataset allowed for assessment of note octave in addition ACKNOWLEDGEMENTS to pitch, allowing for more continuous structure Sample Processed Beethoven Midi Segment • Clear tradeoff between repetition and structure dependent on Huge thanks to Zach 2. Song Generation window length in classical music • Clearer musical progressions from classical music data set, most Barnes for guidance • Key value from map selected as starting time segment likely due to octave information and reduced noise during the project and • Successive notes chosen as window changes • Incorporation of domain knowledge achieved better musicality (key to Percy Liang/Stefano • Structured insertion of domain knowledge such as (+ common vs. uncommon notes in key), starting and ending on the Ermon/the teaching starting and ending on same note note of the given key, etc.) staff for the course