Fetching the paper…
Reading the bibliography…
Automatic music transcription (AMT) is the task of transcribing audio recordings into symbolic representations.
E. Scheirer, “Using musical knowledge to extract expressive performance information from audio recordings.",” in IJCAI Workshop on Computational Auditory Scene Analysis , 1995, pp. 153–160
1995
Earlier work this paper cites.
S. Dixon, “On the computer recognition of solo piano music,” in Proceedings of Australasian Computer Music Conference , 2000, pp. 31–37
2000
Earlier work this paper cites.
M. Marolt, “Transcription of polyphonic piano music with neural networks,” in Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries , vol. 2, 2000, pp. 512–515
2000
Earlier work this paper cites.
W. Goebl, “Melody lead in piano performance: Expressive device or artifact?” The Journal of the Acoustical Society of America , vol. 110, no. 1, pp. 563–572, 2001
2001
Earlier work this paper cites.
C. Raphael, “Automatic transcription of piano music,” in International Society for Music Information Retrieval (ISMIR) , 2002
2002
Earlier work this paper cites.
Goebl, Werner, “The role of timing and intensity in the production and perception of melody in expressive piano performance,” PhD Thesis , 2003
2003
Earlier work this paper cites.
S. A. Abdallah and M. D. Plumbley, “Polyphonic music transcription by non-negative sparse coding of power spectra,” in International Conference on Music Information Retrieval (ISMIR) , 2004, pp. 318–325
2004
Earlier work this paper cites.
M. Marolt, “A connectionist approach to automatic transcription of polyphonic piano music,” IEEE Transactions on Multimedia , vol. 6, no. 3, pp. 439–449, 2004
2004
Earlier work this paper cites.
G. E. Poliner and D. P. W. Ellis, “A discriminative model for polyphonic piano transcription,” EURASIP Journal on Advances in Signal Processing , 2006
2006
Earlier work this paper cites.
J. P. Bello, L. Daudet, and M. Sandler, “Automatic piano transcription using frequency and time-domain information,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 14, no. 6, pp. 2242–2251, 2006
2006
Earlier work this paper cites.
R. B. Dannenberg, “The interpretation of midi velocity,” in International Computer Music Conference (ICMC) , 2006
2006
Earlier work this paper cites.
V. Emiya, R. Badeau, and B. David, “Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 18, no. 6, pp. 1643–1654, 2009
2009
Earlier work this paper cites.
B. Niedermayer and G. Widmer, “A multi-pass algorithm for accurate audio-to-score alignment.” in International Society for Music Information Retrieval (ISMIR) , 2010, pp. 417–422
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of International Conference on Machine Learning (ICML) , 2010, pp. 807–814
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
Z. Duan and B. Pardo, “Soundprism: An online system for score-informed source separation of music audio,” IEEE Journal of Selected Topics in Signal Processing , vol. 5, no. 6, pp. 1205–1215, 2011
2011
Cited alongside, same era.
J. Nam, J. Ngiam, H. Lee, and M. Slaney, “A classification-based polyphonic piano transcription approach using learned feature representations.” in International Society for Music Information Retrieval (ISMIR) , 2011, pp. 175–180
2011
Cited alongside, same era.
S. Böck and M. Schedl, “Polyphonic piano note transcription with recurrent neural networks,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2012, pp. 121–124
2012
Cited alongside, same era.
E. Benetos, S. Dixon, D. Giannoulis, H. Kirchhoff, and A. Klapuri, “Automatic music transcription: challenges and future directions,” Journal of Intelligent Information Systems , vol. 41, no. 3, pp. 407–434, 2013
2013
Cited alongside, same era.
R. Kelz, M. Dorfer, F. Korzeniowski, S. Böck, A. Arzt, and G. Widmer, “On the potential of simple framewise approaches to piano transcription,” in International Society for Music Information Retrieval (ISMIR) , 2016
2016
Later among the works it cites.
A. Gkiokas and V. Katsouros, “Convolutional neural networks for real-time beat tracking: A dancing robot application.” in International Society for Music Information Retrieval (ISMIR) , 2017, pp. 286–293
2017
Later among the works it cites.
R. M. Bittner, B. McFee, J. Salamon, P. Li, and J. P. Bello, “Deep salience representations for F0 estimation in polyphonic music.” in International Society for Music Information Retrieval (ISMIR) , 2017, pp. 63–70
2017
Later among the works it cites.
E. Benetos, S. Dixon, Z. Duan, and S. Ewert, “Automatic music transcription: An overview,” IEEE Signal Processing Magazine , vol. 36, no. 1, pp. 20–30, 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. O’Hanlon and M. D. Plumbley, “Polyphonic piano transcription using non-negative matrix factorisation with group sparsity,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2014, pp. 3112–3116
2014
Cited alongside, same era.
T. Berg-Kirkpatrick, J. Andreas, and D. Klein, “Unsupervised transcription of piano music,” in Advances in Neural Information Processing Systems (NeurIPS) , 2014, pp. 1538–1546
2014
Cited alongside, same era.
K. Ullrich, J. Schlüter, and T. Grill, “Boundary detection in music structure analysis using convolutional neural networks.” in International Society for Music Information Retrieval (ISMIR) , 2014, pp. 417–422
2014
Cited alongside, same era.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Cited alongside, same era.
A. Cogliati, Z. Duan, and B. Wohlberg, “Piano music transcription with fast convolutional sparse coding,” in IEEE International Workshop on Machine Learning for Signal Processing (MLSP) , 2015, pp. 1–6
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (ICML) , 2015, pp. 448–456
2015
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR) , 2015
2015
Cited alongside, same era.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 779–788
2016
Cited alongside, same era.
C. Hawthorne, E. Elsen, J. Song, A. Roberts, I. Simon, C. Raffel, J. Engel, S. Oore, and D. Eck, “Onsets and frames: Dual-objective piano transcription,” in International Society for Music Information Retrieval Conference (ISMIR) , 2018
2018
Later among the works it cites.
C. Hawthorne, A. Stasyuk, A. Roberts, I. Simon, C. A. Huang, S. Dieleman, E. Elsen, J. Engel, and D. Eck, “Enabling factorized piano music modeling and generation with the maestro dataset,” International Conference on Learning Representations (ICLR) , 2018
2018
Later among the works it cites.
A. Elowsson, “Modeling music: studies of music transcription, music perception and music production,” KTH Royal Institute of Technology , 2018
2018
Later among the works it cites.
R. Kelz, S. Böck, and C. Widnaer, “Multitask learning for polyphonic piano transcription, a case study,” in IEEE International Workshop on Multilayer Music Representation and Processing (MMRP) , 2019, pp. 85–91
2019
Later among the works it cites.
J. Schroeter, K. Sidorov, and D. Marshall, “Softloc: Robust temporal localization under label misalignment,” Open Review at International Conference on Learning Representations (ICLR) , 2019
2019
Later among the works it cites.
R. Kelz, S. Böck, and G. Widmer, “Deep polyphonic ADSR piano note transcription,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 246–250
2019
Later among the works it cites.
B. Liang, G. Fazekas, and M. Sandler, “Piano sustain-pedal detection using convolutional neural networks,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 241–245
2019
Later among the works it cites.
2019
Later among the works it cites.
J. W. Kim and J. P. Bello, “Adversarial learning for improved onsets and frames music transcription,” in International Society for Music Information Retrieval (ISMIR) , 2019, pp. 670–677
2019
Later among the works it cites.
A. Elowsson, “Polyphonic pitch tracking with deep layered learning,” The Journal of the Acoustical Society of America , vol. 148, no. 1, pp. 446–468, 2020
2020
Closest in time.
Q. Kong, Y. Cao, T. Iqbal, Y. Wang, W. Wang, and M. D. Plumbley, “PANNs: Large-scale pretrained audio neural networks for audio pattern recognition,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 28, pp. 2880–2894, 2020
2020
Closest in time.