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This paper describes a statistically-principled semi-supervised method of automatic chord estimation (ACE) that can make effective use of music signals regardless of the availability of chord annotations.
L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,” Proceedings of the IEEE , vol. 77, no. 2, pp. 257–286, 1989
1989
Earlier work this paper cites.
T. Fujishima, “Realtime chord recognition of musical sound: A system using common lisp music,” in Proceedings ofthe International Computer Music Con- ference (ICMC) , 1999, pp. 464–467
1999
Earlier work this paper cites.
M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, “RWC music database: Popular, classical, and jazz music databases,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2002, pp. 287–288
2002
Earlier work this paper cites.
A. Sheh and D. Ellis, “Chord segmentation and recognition using EM-trained hidden Markov models,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2003, pp. 185–191
2003
Earlier work this paper cites.
A. Berenzweig, B. Logan, D. Ellis, and B. Whitman, “A large-scale evaluation of acoustic and subjective music-similarity measures,” Computer Music Journal , vol. 28, no. 2, pp. 63–76, 2004
2004
Earlier work this paper cites.
2005
Earlier work this paper cites.
J. P. Bello, “Audio-based cover song retrieval using approximate chord sequences: Testing shifts, gaps, swaps and beats.” in proceedings of the International Society for Music Information Retrieval (ISMIR) , vol. 7, 2007, pp. 239–244
2007
Earlier work this paper cites.
K. Lee and M. Slaney, “Acoustic chord transcription and key extraction from audio using key-dependent hmms trained on synthesized audio,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 16, no. 2, pp. 291–301, Feb 2008
2008
Earlier work this paper cites.
A. Anglade, R. Ramirez, and S. Dixon, “Genre classification using harmony rules induced from automatic chord transcriptions.” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2009, pp. 669–674
2009
Earlier work this paper cites.
M. Khadkevich and M. Omogolo, “Use of hidden markov models and factored language models for automatic chord recognition,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2009, pp. 561–566
2009
Earlier work this paper cites.
M. Mauch and S. Dixon, “Approximate note transcription for the improved identification of difficult chords,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2010, pp. 135–140
2010
Earlier work this paper cites.
C. Schörkhuber and A. Klapuri, “Constant-q transform toolbox for music processing,” in 7th Sound and Music Computing Conference , 2010
2010
Earlier work this paper cites.
C. Harte, “Towards automatic extraction of harmony information from music signals,” Ph.D. dissertation, Queen Mary University of London, 2010
2010
Earlier work this paper cites.
M. Goto, K. Yoshii, H. , Fujihara, M. Mauch, and T. Nakano, “Songle: A web service for active music listening improved by user contributions,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2011, pp. 311–316
2011
Earlier work this paper cites.
J. A. Burgoyne, J. Wild, and I. Fujinaga, “An expert ground truth set for audio chord recognition and music analysis,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2011, pp. 633–638
2011
Earlier work this paper cites.
Y. Ni, M. McVicar, R. Santos-Rodriguez, and T. De Bie, “An end-to-end machine learning system for harmonic analysis of music,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 20, no. 6, pp. 1771–1783, 2012
2012
Cited alongside, same era.
R. Chen, W. Shen, A. Srinivasamurthy, and P. Chordia, “Chord recognition using duration-explicit hidden Markov models,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2012, pp. 445–450
2012
Cited alongside, same era.
E. J. Humphrey and J. P. Bello, “Rethinking automatic chord recognition with convolutional neural networks,” in 2012 11th International Conference on Machine Learning and Applications , vol. 2, 2012, pp. 357–362
2012
Cited alongside, same era.
N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent, “Audio chord recognition with recurrent neural networks.” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2013, pp. 335–340
2013
F. Korzeniowski and G. Widmer, “On the futility of learning complex frame-level language models for chord recognition,” in AES Conference on Semantic Audio , 2017, pp. 2–6
2017
Later among the works it cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Łukasz Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems (NeurIPS) , 2017, pp. 5998–6008
2017
Later among the works it cites.
B. Mcfee and J. P. Bello, “Structured training for large-vocabulary chord recognition,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2017, pp. 188–194
2017
Later among the works it cites.
J. Deng and Y. K. Kwok, “Large vocabulary automatic chord estimation with an even chance training scheme,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2017, pp. 531–536
2017
Later among the works it cites.
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Cited alongside, same era.
A. Graves, N. Jaitly, and A. Mohamed, “Hybrid speech recognition with deep bidirectional LSTM,” in 2013 IEEE Workshop on Automatic Speech Recognition and Understanding , Dec 2013, pp. 273–278
2013
Cited alongside, same era.
M. McVicar, R. Santos-Rodriguez, Y. Ni, and T. D. Bie, “Automatic chord estimation from audio: A review of the state of the art,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 22, no. 2, pp. 556–575, 2014
2014
Cited alongside, same era.
D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,” in proceedings of the International Conference on Learning Representations (ICLR) , 2014, pp. 1–14
2014
Cited alongside, same era.
S. Gershman and N. Goodman, “Amortized inference in probabilistic reasoning,” in Proceedings of the annual meeting of the cognitive science society , vol. 36, no. 36, 2014, pp. 517–522
2014
Cited alongside, same era.
D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, “Semi-supervised learning with deep generative models,” in NIPS , 2014, pp. 3581–3589
2014
Cited alongside, same era.
E. J. Humphrey and J. P. Bello, “Four timely insights on automatic chord estimation,” in proceedings of the International Society for Music Information Retrieval , 10 2015, pp. 673–679
2015
Cited alongside, same era.
S. Sigtia, N. Boulanger-Lewandowski, and S. Dixon, “Audio chord recognition with a hybrid recurrent neural network,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2015, pp. 127–133
2015
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in proceedings of the International Conference on Learning Representations (ICLR) , 2015, pp. 1–15
2015
Cited alongside, same era.
E. Jang, S. Gu, and B. Poole, “Categorical reparameterization with Gumbel-Softmax,” in proceedings of the International Conference on Learning Representations (ICLR) , 2017
2017
Later among the works it cites.
——, “Improved chord recognition by combining duration and harmonic language models,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2018, pp. 10–17
2018
Later among the works it cites.
T. Carsault, J. Nika, and P. Esling, “Using musical relationships between chord labels in automatic chord extraction tasks,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2018, pp. 18–25
2018
Later among the works it cites.
E. Dupont, “Learning disentangled joint continuous and discrete representations,” in Advances in Neural Information Processing Systems (NeurIPS) , 2018, pp. 710–720
2018
Later among the works it cites.
J. Pauwels, K. O’Hanlon, E. Gómez, and M. B. Sandler, “20 years of automatic chord recognition from audio,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2019, pp. 54–63
2019
Later among the works it cites.
Y. Wu, T. Carsault, and K. Yoshii, “Automatic chord estimation based on a frame-wise convolutional recurrent neural network with non-aligned annotations,” in 27th European Signal Processing Conference (EUSIPCO) , 2019
2019
Later among the works it cites.
T.-P. Chen and L. Su, “Harmony transformer: Incorporating chord segmentation into harmony recognition,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2019, pp. 259–267
2019
Later among the works it cites.
H. V. Koops, W. B. de Haas, J. A. Burgoyne, J. Bransen, A. Kent-Muller, and A. Volk, “Annotator subjectivity in harmony annotations of popular music,” Journal of New Music Research , vol. 48, no. 3, pp. 232–252, 2019
2019
Later among the works it cites.
J. Jiang, K. Chen, W. Li, and G. Xia, “Large-vocabulary chord transcription via chord structure decomposition,” in proceedings of the International Society for Music Information Retrieval (ISMIR) , 2019, pp. 644–651
2019
Later among the works it cites.
T. Hori, R. F. Astudillo, T. Hayashi, Y. Zhang, S. Watanabe, and J. L. Roux, “Cycle-consistency training for end-to-end speech recognition,” in proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 6271–6275
2019
Later among the works it cites.
Y. Wu and W. Li, “Automatic audio chord recognition with MIDI-trained deep feature and BLSTM-CRF sequence decoding model,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 27, no. 2, pp. 355–366, 2019
2019
Later among the works it cites.