Neural Network Drum Track Composition Dan Smith

Goal Develop a neural net which can be trained to produce drum tracks given a few starting beats.

Previous work Michael Mozer – Neural Network Music composition by prediction Adam Guetz and Tony Lee – Neural Network Music Composition

Network Each note is presented to network, the network predicts next note. Training mode –At each note, error is calculated based on next note –Weights are updated Simulation mode –Next note is fed back into network

Network architecture Next note Context Note selector Neural Network

Note representation Note is respresented at duration and instruments Six instruments: snare, tom1, tom2, cymbal, hi-hat, bass.

Instrument Representation Instruments are represented in binary, 1 means the instrument is being played, 0 means it is not Vector is (snare, tom1, tom2, cymbal, hi- hat, bass)

Duration representation Time is divided into ticks. Each tick represents 1/12 of a beat. –Eighth note = 6 ticks –Eighth note triplet = 4 ticks Note is represented by elapsed time, as well as (elapsed time mod 4) and (elapsed time mod 3)

Why? Eighth notes = sixteenth notes mod 3 –3 mod 3 = 0 –6 mod 3 = 0 Eight note triplets = quarter note triplets mod 4 –4 mod 4 = 0 –8 mod 4 = 0

Duration(cont) Each mod is represented by a 1 hot code –Eg 6 mod 4 = 2 = 0100 –6 mod 3 = 0 = 001

Total representation 14 until vector (duration, mod4 (4 inputs), mod3 (3 inputs), snare, tom1, tom2, cymbal, hi-bat, bass)

Converting Output to Next Note Duration is distributed, must pick real duration Pick duration vector closest to the duration produced by the network

Results Network learned simple patterns without repeated note (analogous to previous work) Network learned patterns with repeated notes Did not generalize well

Future suggestions Work on generalization –Problem with context resetting Try top down production Method

References Michael Mozer – Neural network music composition by prediction – ic.html Tony Lee and Adam Guetz – Neural Network Music compostion –