Novice composers often find it difficult to go beyond common chord progressions. To make it easier for composers to experiment with radical chord choices, we built a creativity support tool, CHORDRIPPLE, which makes chord recommendations that aim to be both diverse and appropriate to the current context. Composers can use it to help select the next chord, or to replace sequences of chords in an internally consistent manner. To make such recommendations, we adapt a neural network model from natural language processing known as WORD2VEC to the music domain. This model learns chord embeddings from a corpus of chord sequences, placing chords nearby when they are used in similar contexts. The learned embeddings support creative substitutions between chords, and also exhibit topological properties that correspond to musical structure. For example, the major and minor chords are both arranged in the latent space in shapes corresponding to the circle-of-fifths. Our structured observations with 14 music students show that the tool helped them explore a wider palette of chords, and to make "big jumps in just a few chords". It gave them "new ideas of ways to move forward in the piece", not just on a chord-to-chord level but also between phrases. Our controlled studies with 9 more music students show that more adventurous chords are adopted when composing with CHORDRIPPLE.
Cheng-Zhi Anna Huang, David Duvenaud, and Krzysztof Z. Gajos. Chordripple: Recommending chords to help novice composers go beyond the ordinary. In Proceedings of the 21st International Conference on Intelligent User Interfaces, IUI '16, pages 241-250, New York, NY, USA, 2016. ACM.