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We introduce a convolutional recurrent neural network (CRNN) for music tagging.
“Automatic musical pattern feature extraction using convolutional neural network,”
Tom LH Li, Antoni B Chan, and A Chun, · 2010
Earlier work this paper cites.
“The million song dataset,”
Thierry Bertin-Mahieux, Daniel PW Ellis, Brian Whitman, and Paul Lamere, · 2011
Earlier work this paper cites.
“Unsupervised learning of local features for music classification.,”
Jan Wülfing and Martin Riedmiller, · 2012
Earlier work this paper cites.
“Deep content-based music recommendation,”
Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen, · 2013
Earlier work this paper cites.
“End-to-end learning for music audio,”
Sander Dieleman and Benjamin Schrauwen, · 2014
Earlier work this paper cites.
“Improved music feature learning with deep neural networks,”
Siddharth Sigtia and Simon Dixon, · 2014
Earlier work this paper cites.
“Dropout: A simple way to prevent neural networks from overfitting,”
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov, · 2014
Earlier work this paper cites.
“On the properties of neural machine translation: Encoder-decoder approaches,”
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio, · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik P. Kingma and Jimmy Ba, · 2014
Cited alongside, same era.
“Document modeling with gated recurrent neural network for sentiment classification,”
Duyu Tang, Bing Qin, and Ting Liu, · 2015
Cited alongside, same era.
“Convolutional recurrent neural networks: Learning spatial dependencies for image representation,”
Zhen Zuo, Bing Shuai, Gang Wang, Xiao Liu, Xingxing Wang, Bing Wang, and Yushi Chen, · 2015
Cited alongside, same era.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
Sergey Ioffe and Christian Szegedy, · 2015
Cited alongside, same era.
“Fast and accurate deep network learning by exponential linear units (elus),”
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter, · 2015
“Keras,”
François Chollet, · 2015
Later among the works it cites.
“Automatic tagging using deep convolutional neural networks,”
Keunwoo Choi, George Fazekas, and Mark Sandler, · 2016
Closest in time.
“Hybrid music recommender using content-based and social information,”
Paulo Chiliguano and Gyorgy Fazekas, · 2016
Closest in time.
“Explaining deep convolutional neural networks on music classification,”
Keunwoo Choi, George Fazekas, and Mark Sandler, · 2016
Closest in time.
“An end-to-end neural network for polyphonic piano music transcription,”
Siddharth Sigtia, Emmanouil Benetos, and Simon Dixon, · 2016
Closest in time.
“Learning to pinpoint singing voice from weakly labeled examples,”
Jan Schlüter, · 2016
Closest in time.
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Cited alongside, same era.
“The power of depth for feedforward neural networks,”
Ronen Eldan and Ohad Shamir, · 2015
Cited alongside, same era.
“librosa: Audio and music signal analysis in python,”
Brian McFee, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto, · 2015
Cited alongside, same era.
“Theano: A python framework for fast computation of mathematical expressions,”
The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, et al., · 2016
Closest in time.