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Chord recognition systems depend on robust feature extraction pipelines.
“Realtime Chord Recognition of Musical Sound: a System Using Common Lisp Music,”
T. Fujishima, · 1999
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“Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,”
J. D. Lafferty, A. McCallum, and F. C. N. Pereira, · 2001
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“RWC Music Database: Popular, Classical and Jazz Music Databases.,”
M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, · 2002
Earlier work this paper cites.
“Chord segmentation and recognition using EM-trained hidden Markov models,”
A. Sheh and D. P. W. Ellis, · 2003
Earlier work this paper cites.
“A Cross-validated Study of Modelling Strategies for Automatic Chord Recognition,”
J. A. Burgoyne, L. Pugin, C. Kereluik, and I. Fujinaga, · 2007
Earlier work this paper cites.
“Conditional neural fields,”
J. Peng, L. Bo, and J. Xu, · 2009
Earlier work this paper cites.
“Neural conditional random fields,”
T. Do and T. Arti, · 2010
Earlier work this paper cites.
“Deep sparse rectifier neural networks,”
X. Glorot, A. Bordes, and Y. Bengio, · 2011
Earlier work this paper cites.
“Time-frequency reassigned features for automatic chord recognition,”
M. Khadkevich and M. Omologo, · 2011
Earlier work this paper cites.
“Chord recognition using duration-explicit hidden Markov models,”
R. Chen, W. Shen, A. Srinivasamurthy, and P. Chordia, · 2012
Cited alongside, same era.
“Learning a robust tonnetz-space transform for automatic chord recognition,”
E. J. Humphrey, T. Cho, and J. P. Bello, · 2012
Cited alongside, same era.
“Rethinking Automatic Chord Recognition with Convolutional Neural Networks,”
E. J. Humphrey and J. P. Bello, · 2012
Cited alongside, same era.
“Using Hyper-genre Training to Explore Genre Information for Automatic Chord Estimation.,”
Y. Ni, M. McVicar, R. Santos-Rodriguez, and T. De Bie, · 2012
Cited alongside, same era.
“Audio chord recognition with recurrent neural networks,”
N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent, · 2013
Cited alongside, same era.
“Dropout: A Simple Way to Prevent Neural Networks from Overfitting,”
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, · 2014
Later among the works it cites.
“Very deep convolutional networks for large-scale image recognition,”
K. Simonyan and A. Zisserman, · 2014
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“Automatic Chord Estimation from Audio: A Review of the State of the Art,”
M. McVicar, R. Santos-Rodriguez, Y. Ni, and T. D. Bie, · 2014
Later among the works it cites.
“Adam: A method for stochastic optimization,”
D. Kingma and J. Ba, · 2014
Later among the works it cites.
“mir_eval: a transparent implementation of common MIR metrics,”
C. Raffel, B. McFee, E. J. Humphrey, J. Salamon, O. Nieto, D. Liang, and D. P. W. Ellis, · 2014
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M. Lin, Q. Chen, and S. Yan, · 2013
Cited alongside, same era.
“Representation Learning: A Review and New Perspectives,”
Y. Bengio, A. Courville, and P. Vincent, · 2013
Cited alongside, same era.
“Automatic chord recognition based on the probabilistic modeling of diatonic modal harmony,”
B. Di Giorgi, M. Zanoni, A. Sarti, and S. Tubaro, · 2013
Cited alongside, same era.
Improved Techniques for Automatic Chord Recognition from Music Audio Signals
T. Cho, · 2014
Cited alongside, same era.
“Four timely insights on automatic chord estimation,”
E. J. Humphrey and J. P. Bello, · 2015
Later among the works it cites.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
S. Ioffe and C. Szegedy, · 2015
Later among the works it cites.
“Feature learning for chord recognition: the deep chroma extractor,”
F. Korzeniowski and G. Widmer, · 2016
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