MAIA Suggest!

Use the inputs below to experiment with outputs from MAIA Markov, an algorithm developed by Music Artificial Intelligence Algorithms, Inc.
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With strong ties to academia, MAIA considers very seriously the fair and appropriate use of material from others. For the Euro {melody, chords, bass} material we used in this project, we conducted "originality analyses" (Yin, Stepney, Reuben & Collins, 2021; Collins & Laney, 2017) using the cardinality score (Janssen, Collins, & Ren, 2019), as well as other analyses, to establish that generated passages did not take too much consecutive material from a single, original, input song.


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Artificial intelligence (AI) has been defined as

"The performance of tasks, which, if performed by a human, would be deemed to require intelligence" (Wiggins, 2006, p.450),
reading between the lines somewhat of Turing's (1950) famous paper. AI methods can be applied to multiple music-analytic and music-creative activities. Here, we consider AI applied to the activity of songwriting.

A main topic of Collins' (2011) PhD and a couple of subsequent papers (Collins et al., 2016; Collins & Laney, 2017) was concerned with automatically extracting the entire hierarchical structure from an existing piece of music, and using that structure to guide a local generative process, so that generated material had more convincing long-term structure and phrasing, compared to existing work.

For this interface however, it seemed more appropriate to use the MAIA Markov algorithm to generate shorter, four-bar passages for human musicians to experiment with and combine into longer-term structures.


This interface arose out of a collaboration with Imogen Heap. Imogen and Tom had a long chat in early February 2020, and one of the topics we covered was the questionable ethics and legality of deriving new material from existing, copyright material without permission and/or proper attribution.

In Tom's opinion, the ethical and legal implications of deriving new from old material centres on a perceptually valid empirical analysis (such as the "originality" or "creativity" analyses conducted in Yin, Stepney, Reuben & Collins, 2021 or Collins & Laney, 2017, section 3.3). If it can be shown that a generated passage is no more derivative of a corpus than other songs from that same corpus, then the reuse of material is acceptable. If a generated passage does derive more from a corpus than other songs, then the resue is not acceptable.

For example, one can't copyright an isochronous bass-drum pattern because such patterns are ubiquitous, whereas one can copyright a sequence of events (notes, chords, etc.) that is novel with respect to the corpus of existing music. Existing research on distinctivity is also worth a read here (Collins et al. 2016; Conklin, 2010).


The source code for these models is available as an NPM package called MAIA Markov, v.0.0.30. The metadata (md), state-transition matrices (stm), and initial distributions (id) that combine with MAIA Markov to enable replication of the results above can be downloaded via the following links.

AI details

The MAIA Markov algorithm uses an empirically-derived Markov model to generate "new" music in the style of a corpus of existing music. An article we wrote for Significance magazine provides a good lay- or young person's introduction to how this works (Collins, Laney, Willis, & Garthwaite, 2011). If/when you're ready for heavier stuff, the main references are my PhD thesis (Collins, 2011) and two subsequent papers (Collins et al., 2016; Collins & Laney, 2017). Of these, Collins and Laney (2017) is probably the best starting point.

I have made some tweaks and improvements to the algorithm since 2017, and I am currently writing these up for a peer-reviewed journal, but the main approach is the same. It is worth noting that a Markov model is not a deep learning algorithm – or even an artificial neural network, both of which are very popular right now. I do conduct research on these approaches too, but so far I prefer the Markov modeling approach because it has stronger cognitive plausibility in terms of the way I learnt to create music – analysing states (e.g., notes or chords) in a certain tonal and temporal context, and formulating and exploring possibilities for how certain states tend to lead to others.

  1. Collins, T. (2011). Improved methods for pattern discovery in music, with applications in automated stylistic composition. PhD thesis, Faculty of Mathematics, Computing and Technology, The Open University.
  2. Collins, T., Arzt, A., Frostel, H., & Widmer, G. (2016). Using geometric symbolic fingerprinting to discover distinctive patterns in polyphonic music corpora. In David Meredith (Ed), Computational Music Analysis, pp. 445-474, Berlin, 2016. Springer
  3. Collins, T., & Laney, R. (2017). Computer-generated stylistic compositions with long-term repetitive and phrasal structure. Journal of Creative Music Systems, 1(2).
  4. Collins, T., & Laney, R., & Willis, A., & Garthwaite, P. H. (2016). Developing and evaluating computational models of musical style. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 30(1), 16-43.
  5. Collins, T., & Laney, R., & Willis, A., & Garthwaite, P. H. (2011). Chopin, mazurkas and Markov. Significance, 8(4), 154-159. Supporting material
  6. Conklin, D. (2010). Discovery of distinctive patterns in music. Intelligent Data Analysis, 14(5):547–554.
  7. Janssen, B., & Collins, T., & Ren, I. (2019). Algorithmic ability to predict the musical future: Datasets and evaluation. In Proceedings of the International Society for Music Information Retrieval Conference, pp. 208-215, Delft. International Society for Music Information Retrieval.
  8. Turing, I. B. A. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.
  9. Wiggins, G. A. (2006). A preliminary framework for description, analysis and comparison of creative systems. Knowledge-Based Systems, 19(7), 449-458.

Contact and credits

We hope you enjoyed experimenting with the outputs of the above project.
Feel free to get in touch if you have any questions or suggestions.
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