Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models

Music composition, like most art forms, has for a long time been a skill specific to human beings. Music composition has an intuitive side to it necessary to determine which pitches create harmorny together, what chords can be played after a certain note, or what note progressions are in violation of intrinsic musical theory. With the recent successes in neural network modeling of predictive natural behaviour and generative models, there have been good applications of modelling note progression probabilities for music generation. The two dominant approaches to neural music generation are adversarial training Sutskever et al. [2014], Liu and Randall [2016], Yang et al. [2017], Dong et al. [2018], and sequence-to-sequence recurrent networks Chung et al. [2014], Waite [2016], Weel [2017], each with its merits. Although Wave-form representations have been shown to be a viable way to generate audio not necessarily specific to music Oord et al. [2016a], it is symbolic representations that are favoured in literature for the task of music generation Mogren [2016], Yang et al. [2017], Lerdahl and Jackendoff [1983], Colombo and Gerstner [2018], Chung et al. [2014]. Owing to the existing lack of out-right comparisons between adversarial and non-adversarial training for music generation, the aim of this study is to compare music samples generated by two generative models, one trained in an adversarial setting, and the other in a non adversarial setting, using musical instrument digital interface (MIDI) data.