This thesis investigates Bach's composition style using deep sequence learning. We develop BachBot: an automatic stylistic composition system for composing polyphonic music in the style of Bach's chorales. We find a 3-layer stacked LSTM performs best and conduct analyses and evaluations to understand its success and failure modes. Unlike many previous works, we avoid allowing prior assumptions about music impact model design, opting instead to build systems that learn rather than ones which encode prior hypotheses. While this is not the first application of deep LSTM to Bach chorales, our work consists of the following novel contributions.

This is a 30 minute talk from GOTO Copenhagen 2019 by Feynman Liang - Creator of BachBot. I've dropped the full talk abstract below for a read before diving into the talk: Can musical creativity, something believed to be deeply human, be codified into an algorithm? While most music theorists are hesitant to claim a "correct" algorithm for composing music like Bach, recent advances in machine learning and computational musicology may help us reach an answer. In this talk, we describe BachBot: an artificial intelligence which uses deep learning and long short term memory (LSTM) to compose music in the style of Bach. We train BachBot on all known Bach chorale harmonisations and carry out the largest musical Turing test to date.