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In this paper, we propose a recurrent neural network (RNN)-based MIDI musiccomposition machine that is able to learn musical knowledge from existingBeatles' songs and generate music in the style of the Beatles with little humanintervention. In the learning stage, a sequence of stylistically uniform,multiple-channel music samples was modeled by a RNN. In the composition stage,a short clip of randomly-generated music was used as a seed for the RNN tostart music score prediction. To form structured music, segments of generatedmusic from different seeds were concatenated together. To improve the qualityand structure of the generated music, we integrated music theory knowledge intothe model, such as controlling the spacing of gaps in the vocal melody,normalizing the timing of chord changes, and requiring notes to be related tothe song's key (C major, for example). This integration improved the quality ofthe generated music as verified by a professional composer. We also conducted asubjective listening test that showed our generated music was close to originalmusic by the Beatles in terms of style similarity, professional quality, andinterestingness. Generated music samples are at https://goo.gl/uaLXoB.

BandNet: A Neural Network-based, Multi-Instrument Beatles-Style MIDI Music Composition Machine