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Symbolic music datasets are important for music information retrieval and musical analysis.
The structure of atonal music
Forte, A. (1973) · 1973
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The ‘USI’, or Universal Synthesizer Interface
Smith, D. and Wood, C. (1981) · 1981
Earlier work this paper cites.
Multiple viewpoint systems for music prediction
Conklin, D. and Witten, I. H. (1995) · 1995
Earlier work this paper cites.
Classical Piano MIDI Page
Krueger, B. (1996) · 1996
Earlier work this paper cites.
The art of inaccuracy: Why pianists’ errors are difficult to hear
Repp, B. H. (1996) · 1996
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Classical Archives
Classical Archives (2000) · 2000
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The challenge of optical music recognition
Bainbridge, D. and Bell, T. (2001) · 2001
Earlier work this paper cites.
MusicXML: An internet-friendly format for sheet music
Good, M. et al. (2001) · 2001
Earlier work this paper cites.
The piano roll: a valuable recording medium of the twentieth century
Bryner, B. (2002) · 2002
Earlier work this paper cites.
Kunstderfuge
Kunstderfuge (2002) · 2002
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The music encoding initiative (MEI)
Roland, P. (2002) · 2002
Earlier work this paper cites.
Lilypond, a system for automated music engraving
Nienhuys, H.-W. and Nieuwenhuizen, J. (2003) · 2003
Earlier work this paper cites.
Online database of scores in the humdrum file format
Sapp, C. S. (2005) · 2005
Earlier work this paper cites.
International Music Score Library Project
IMSLP (2006) · 2006
Earlier work this paper cites.
Content-based music information retrieval: Current directions and future challenges
Casey, M. A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., and Slaney, M. (2008) · 2008
Cited alongside, same era.
A new music database describing deviation information of performance expressions
Hashida, M., Matsui, T., and Katayose, H. (2008) · 2008
Cited alongside, same era.
Multiple fundamental frequency estimation by modeling spectral peaks and non-peak regions
Duan, Z., Pardo, B., and Zhang, C. (2010) · 2010
Cited alongside, same era.
Maps-a piano database for multipitch estimation and automatic transcription of music
Emiya, V., Bertin, N., David, B., and Badeau, R. (2010) · 2010
Cited alongside, same era.
Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y. (2011) · 2011
Cited alongside, same era.
Unfolding the potential of computational musicology
MidiNet: A convolutional generative adversarial network for symbolic-domain music generation
Yang, L., Chou, S., and Yang, Y. (2017) · 2017
Later among the works it cites.
Computational models of expressive music performance: A comprehensive and critical review
Cancino-Chacón, C. E., Grachten, M., Goebl, W., and Widmer, G. (2018) · 2018
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Onsets and frames: Dual-objective piano transcription
Hawthorne, C., Elsen, E., Song, J., Roberts, A., Simon, I., Raffel, C., Engel, J., Oore, S., and Eck, D. (2018) · 2018
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Music transformer: Generating music with long-term structure
Huang, C.-Z. A., Vaswani, A., Uszkoreit, J., Simon, I., Hawthorne, C., Shazeer, N., Dai, A. M., Hoffman, M. D., Dinculescu, M., and Eck, D. (2018) · 2018
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Creating a multitrack classical music performance dataset for multimodal music analysis: Challenges, insights, and applications
Li, B., Liu, X., Dinesh, K., Duan, Z., and Sharma, G. (2018) · 2018
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Volk, A., Wiering, F., and K., P. K. (2011) · 2011
Cited alongside, same era.
Optical music recognition: state-of-the-art and open issues
Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A. R., Guedes, C., and Cardoso, J. S. (2012) · 2012
Cited alongside, same era.
Using of Jaccard coefficient for keywords similarity
Niwattanakul, S., Singthongchai, J., Naenudorn, E., and Wanapu, S. (2013) · 2013
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
Cited alongside, same era.
Computational music analysis
Meredith, D. (2016) · 2016
Cited alongside, same era.
Learning-based methods for comparing sequences, with applications to audio-to-midi alignment and matching
Raffel, C. (2016) · 2016
Cited alongside, same era.
A tutorial on deep learning for music information retrieval
Choi, K., Fazekas, G., Cho, K., and Sandler, M. (2017) · 2017
Cited alongside, same era.
Enabling factorized piano music modeling and generation with the maestro dataset
Hawthorne, C., Stasyuk, A., Roberts, A., Simon, I., Huang, C. A., Dieleman, S., Elsen, E., Engel, J., and Eck, D. (2019) · 2019
Later among the works it cites.
The Bach Doodle: Approachable music composition with machine learning at scale
Huang, C.-Z. A., Hawthorne, C., Roberts, A., Dinculescu, M., Wexler, J., Hong, L., and Howcroft, J. (2019) · 2019
Later among the works it cites.
Adversarial learning for improved onsets and frames music transcription
Kim, J. W. and Bello, J. P. (2019) · 2019
Later among the works it cites.
Supra: Digitizing the stanford university piano roll archive
Shi, Z., Sapp, C. S., Arul, K., McBride, J., and Smith III, J. O. (2019) · 2019
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ASAP: a dataset of aligned scores and performances for piano transcription
Foscarin, F., Mcleod, A., Rigaux, P., Jacquemard, F., and Sakai, M. (2020) · 2020
Closest in time.
PANNs: Large-scale pretrained audio neural networks for audio pattern recognition
Kong, Q., Cao, Y., Iqbal, T., Wang, Y., Wang, W., and Plumbley, M. D. (2020) · 2020
Closest in time.
Polyphonic piano transcription using autoregressive multi-state note model
Kwon, T., Jeong, D., and Nam, J. (2020) · 2020
Closest in time.
High-resolution piano transcription with pedals by regressing onsets and offsets times
Kong, Q., Li, B., Song, X., Wan, Y., and Wang, Y. (2021) · 2021
Closest in time.