A combined model of sensory and cognitive representations underlying tonal expectations in music: From audio signals to behavior. T Collins, B Tillmann, FS Barrett, C Delbé, P Janata Psychological Review 121 (1), 33, 2014 | 109 | 2014 |
Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation PMC Harrison, T Collins, D Müllensiefen Scientific Reports 7 (1), 3618, 2017 | 79 | 2017 |
What is a podcast? Considering innovations in podcasting through the six-tensions framework J Rime, C Pike, T Collins Convergence 28 (5), 1260-1282, 2022 | 74 | 2022 |
SIARCT-CFP: Improving Precision and the Discovery of Inexact Musical Patterns in Point-Set Representations. T Collins, A Arzt, S Flossmann, G Widmer ISMIR, 549-554, 2013 | 66 | 2013 |
Improved methods for pattern discovery in music, with applications in automated stylistic composition T Collins The Open University, 2011 | 60 | 2011 |
Developing and evaluating computational models of musical style T Collins, R Laney, A Willis, PH Garthwaite Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1-28, 2015 | 57 | 2015 |
A comparative evaluation of algorithms for discovering translational patterns in Baroque keyboard works T Collins, J Thurlow, R Laney, A Willis, P Garthwaite | 48 | 2010 |
Impaired maintenance of interpersonal synchronization in musical improvisations of patients with borderline personality disorder K Foubert, T Collins, J De Backer Frontiers in Psychology 8, 537, 2017 | 46 | 2017 |
Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio T Collins, S Böck, F Krebs, G Widmer Audio Engineering Society Conference: 53rd International Conference …, 2014 | 45 | 2014 |
Computer–generated stylistic compositions with long–term repetitive and phrasal structure T Collins, R Laney Journal of Creative Music Systems 1 (2), 2017 | 35 | 2017 |
Modeling Pattern Importance in Chopin's Mazurkas T Collins, R Laney, A Willis, PH Garthwaite Music Perception 28 (4), 387-414, 2011 | 26 | 2011 |
A Brief Review of Creative MIR E Humphery, D Trunbull, T Collins Int. Conf. on Music Information Retrieval late-breaking news and demos, 2013 | 24 | 2013 |
Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora T Collins, A Arzt, H Frostel, G Widmer Computational Music Analysis, 445-474, 2016 | 23 | 2016 |
Discovery of repeated themes and sections T Collins Retrieved 4th May, http://www. musicir. org/mirex/wiki/2013: Discovery of …, 2013 | 19 | 2013 |
Deep learning’s shallow gains: a comparative evaluation of algorithms for automatic music generation Z Yin, F Reuben, S Stepney, T Collins Machine Learning 112 (5), 1785-1822, 2023 | 13 | 2023 |
“A Good Algorithm Does Not Steal–It Imitates”: The Originality Report as a Means of Measuring When a Music Generation Algorithm Copies Too Much Z Yin, F Reuben, S Stepney, T Collins Artificial Intelligence in Music, Sound, Art and Design: 10th International …, 2021 | 12 | 2021 |
Analyzing and classifying guitarists from rock guitar solo tablature O Das, B Kaneshiro, T Collins Proceedings of the Sound and Music Computing Conference, Limassol, Chypre, 2018 | 10 | 2018 |
Constructing Maskanda T Collins South African Music Studies 26 (1), 1-26, 2008 | 10 | 2008 |
Algorithmic Ability to Predict the Musical Future: Datasets and Evaluation. B Janssen, T Collins, IY Ren ISMIR, 208-215, 2019 | 9 | 2019 |
Chopin, mazurkas and Markov: Making music in style with statistics T Collins, R Laney, A Willis, PH Garthwaite Significance 8 (4), 154-159, 2011 | 9 | 2011 |