| Implementing “A generative theory of tonal music” M Hamanaka, K Hirata, S Tojo Journal of New Music Research 35 (4), 249-277, 2006 | 219 | 2006 |
| Melody morphing method based on GTTM M Hamanaka, K Hirata, S Tojo ICMC, 155-158, 2008 | 83 | 2008 |
| CGBVS‐DNN: prediction of compound‐protein interactions based on deep learning M Hamanaka, K Taneishi, H Iwata, J Ye, J Pei, J Hou, Y Okuno Molecular informatics 36 (1-2), 1600045, 2017 | 78 | 2017 |
| FATTA: Full automatic time-span tree analyzer M Hamanaka, K Hirata, S Tojo ICMC 1, 153-156, 2007 | 61 | 2007 |
| ATTA: Automatic Time-Span Tree Analyzer Based on Extended GTTM. M Hamanaka, K Hirata, S Tojo ISMIR 5, 358-365, 2005 | 57 | 2005 |
| Musical structural analysis database based on GTTM M Hamanaka, K Hirata, S Tojo ISMIR 2014, 2014 | 56 | 2014 |
| Tree-structured probabilistic model of monophonic written music based on the generative theory of tonal music E Nakamura, M Hamanaka, K Hirata, K Yoshii 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 46 | 2016 |
| Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music M Hamanaka, K Hirata, S Tojo Computational music analysis, 221-249, 2016 | 37 | 2016 |
| GTTM III: Learning-Based Time-Span Tree Generator Based on PCFG M Hamanaka, K Hirata, S Tojo International Symposium on Computer Music Multidisciplinary Research, 387-404, 2015 | 37 | 2015 |
| 音楽理論 GTTM に基づくグルーピング構造獲得システム 浜中雅俊， 平田圭二， 東条敏 情報処理学会, 2007 | 36 | 2007 |
| A learning-based quantization: Unsupervised estimation of the model parameters M Hamanaka, M Goto, H Asoh, N Otsu ICMC, 2003 | 34 | 2003 |
| Automatic Generation of Grouping Structure based on the GTTM M Hamanaka, K Hirata, S Tojo ICMC, 2004 | 32 | 2004 |
| deepgttm-iii: Multi-task learning with grouping and metrical structures M Hamanaka, K Hirata, S Tojo Music Technology with Swing: 13th International Symposium, CMMR 2017 …, 2018 | 30 | 2018 |
| Interactive Gttm Analyzer. M Hamanaka, S Tojo ISMIR, 291-296, 2009 | 30 | 2009 |
| A learning-based jam session system that imitates a player's personality model M Hamanaka, M Goto, H Asoh, N Otsu International Joint Conference on Artificial Intelligence 18, 51-58, 2003 | 29 | 2003 |
| Cognitive Similarity grounded by tree distance from the analysis of K. 265/300e K Hirata, S Tojo, M Hamanaka International Symposium on Computer Music Multidisciplinary Research, 589-605, 2013 | 25 | 2013 |
| deepGTTM-I&II: Local boundary and metrical structure analyzer based on deep learning technique M Hamanaka, K Hirata, S Tojo Bridging People and Sound: 12th International Symposium, CMMR 2016, São …, 2017 | 24 | 2017 |
| Use of decision tree to detect GTTM group boundaries Y Miura, M Hamanaka, K Hirata, S Tojo Future University Hakodate, 2009 | 23 | 2009 |
| Melody extrapolation in GTTM approach M Hamanaka, K Hirata, S Tojo ICMC, 2009 | 22 | 2009 |
| Method to detect GTTM local grouping boundaries based on clustering and statistical learning K Kanamori, M Hamanaka, J Hoshino ICMC, 2014 | 20 | 2014 |
