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Sig KDD 2017 Watson Beat poster
- 1. https://soundcloud.com/jmukund/sets/watsonbeat-ml4creativity-2017 Watson Beat Janani Mukundan • jmukund@us.ibm.com Richard Daskas • daskas@us.ibm.com Watson Beat, is a cognitive system that composes original music based on its knowledge of music theory. One of the underlying technologies used in Watson Beat is reinforcement learning agent that can learn chords and melodies. Watson Beat also employs a deep belief network (stacked RBMs) that can learn melodies. Reinforcement learning (RL) studies how autonomous agents situated in probabilistic environments learn to maximize a long term goal. Watson Beat: An unsupervised music composition engine that fits into the framework of an RL system. • The composer is the RL agent. • The environment can include relevant state attributes like (a) the current chord being played, (b) the current scale being used, (c) the tempo etc. • The composer senses the state of the system, chooses the next note to be played, and is assigned a numerical immediate reward. • The goal of the composer is to maximize an objective function (value function) like (a) generate a simple four bar chord progression, (b) generate a syncopated eight bar melody etc. Watson Beat RLAgentIntroduction Background • The basic structure of an RL system consists of a stochastic agent and its environment. • The RL agent interacts with its environment in discrete time steps, senses its current state, performs an action, is rewarded for the action, and moves to another state. • The objective of the RL agent is to maximize its long-term cumulative reward by interacting with its environment and learning an optimal policy that maps states to actions. RL Based Algorithms For Chord Generation And Melody Composition Results Conclusion We introduce Watson Beat – a cognitive engine that composes music using the principles of reinforcement learning and music theory. We detail two case studies (a) chord generation, and (b) melody composition, based on these principles. The ratio of chord tones to non-chord tones seen for each episode when training the RL based melody generator described when the complexity of the melody is set to (a) simple, and (b) semi-complex IBM Research, Austin