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Simon Colton

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10 papers
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10

ICLR Conference 2025 Conference Paper

Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling

  • Louis Bradshaw
  • Simon Colton

We introduce an extensive new dataset of MIDI files, created by transcribing audio recordings of piano performances into their constituent notes. The data pipeline we use is multi-stage, employing a language model to autonomously crawl and score audio recordings from the internet based on their metadata, followed by a stage of pruning and segmentation using an audio classifier. The resulting dataset contains over one million distinct MIDI files, comprising roughly 100,000 hours of transcribed audio. We provide an in-depth analysis of our techniques, offering statistical insights, and investigate the content by extracting metadata tags, which we also provide. Dataset available at https://github.com/loubbrad/aria-midi.

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AAAI Conference 2025 Conference Paper

Text2midi: Generating Symbolic Music from Captions

  • Keshav Bhandari
  • Abhinaba Roy
  • Kyra Wang
  • Geeta Puri
  • Simon Colton
  • Dorien Herremans

This paper introduces text2midi, an end-to-end model to generate MIDI files from textual descriptions. Leveraging the growing popularity of multimodal generative approaches, text2midi capitalizes on the extensive availability of textual data and the success of large language models (LLMs). Our end-to-end system harnesses the power of LLMs to generate symbolic music in the form of MIDI files. Specifically, we utilize a pretrained LLM encoder to process captions, which then condition an autoregressive transformer decoder to produce MIDI sequences that accurately reflect the provided descriptions. This intuitive and user-friendly method significantly streamlines the music creation process by allowing users to generate music pieces using text prompts. We conduct comprehensive empirical evaluations, incorporating both automated and human studies, that show our model generates MIDI files of high quality that are indeed controllable by text captions that may include music theory terms such as chords, keys, and tempo.

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AAAI Conference 2023 System Paper

A Tool for Generating Controllable Variations of Musical Themes Using Variational Autoencoders with Latent Space Regularisation

  • Berker Banar
  • Nick Bryan-Kinns
  • Simon Colton

A common musical composition practice is to develop musical pieces using variations of musical themes. In this study, we present an interactive tool which can generate variations of musical themes in real-time using a variational autoencoder model. Our tool is controllable using semantically meaningful musical attributes via latent space regularisation technique to increase the explainability of the model. The tool is integrated into an industry standard digital audio workstation - Ableton Live - using the Max4Live device framework and can run locally on an average personal CPU rather than requiring a costly GPU cluster. In this way we demonstrate how cutting-edge AI research can be integrated into the exiting workflows of professional and practising musicians for use in the real-world beyond the research lab.

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NeSy Conference 2022 Conference Paper

Towards Educating Artificial Neural Systems

  • Simon Colton

In the context of text-to-image generators, we discuss how a neural system could be enhanced to follow a knowledge base expressing certain moral considerations, in order to address some looming concerns. We also propose self-educating procedures that enable the system to produce rule-based approximations of its functioning, and illustrate this with an application to the creative interpretation of images.

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ECAI Conference 2012 Conference Paper

Computational Creativity: The Final Frontier?

  • Simon Colton
  • Geraint A. Wiggins

Notions relating to computational systems exhibiting creative behaviours have been explored since the very early days of computer science, and the field of Computational Creativity research has formed in the last dozen years to scientifically explore the potential of such systems. We describe this field via a working definition; a brief history of seminal work; an exploration of the main issues, technologies and ideas; and a look towards future directions. As a society, we are jealous of our creativity: creative people and their contributions to cultural progression are highly valued. Moreover, creative behaviour in people draws on a full set of intelligent abilities, so simulating such behaviour represents a serious technical challenge for Artificial Intelligence research. As such, we believe it is fair to characterise Computational Creativity as a frontier for AI research beyond all others-maybe, even, the final frontier.

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ECAI Conference 2006 Conference Paper

Automatic Generation of Implied Constraints

  • John William Charnley
  • Simon Colton
  • Ian Miguel

A well-known difficulty with solving Constraint Satisfaction Problems (CSPs) is that, while one formulation of a CSP may enable a solver to solve it quickly, a different formulation may take prohibitively long to solve. We demonstrate a system for automatically reformulating CSP solver models by combining the capabilities of machine learning and automated theorem proving with CSP systems. Our system is given a basic CSP formulation and outputs a set of reformulations, each of which includes additional constraints. The additional constraints are generated through a machine learning process and are proven to follow from the basic formulation by a theorem prover. Experimenting with benchmark problem classes from finite algebras, we show how the time invested in reformulation is often recovered many times over when searching for solutions to more difficult problems from the problem class.

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IJCAI Conference 1999 Conference Paper

Automatic Concept Formation in Pure Mathematics

  • Simon Colton
  • Alan Bundy
  • Toby Walsh

The HR program forms concepts and makes conjectures in domains of pure mathematics and uses theorem prover OTTER and model generator MACE to prove or disprove the conjectures. HR measures properties of concepts and assesses the theorems and proofs involving them to estimate the interestingness of each concept and employ a best first search. This approach has led HR to the discovery of interesting new mathematics and enables it to build theories from just the axioms of finite algebras.

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