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Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm.
“Gradient-based learning applied to document recognition,”
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner, · 1998
Earlier work this paper cites.
“Mel frequency cepstral coefficients for music modeling.,”
Beth Logan et al., · 2000
Earlier work this paper cites.
“Machine hearing: An emerging field [exploratory dsp],”
Richard F Lyon, · 2010
Earlier work this paper cites.
“On the stratification of multi-label data,”
Konstantinos Sechidis, Grigorios Tsoumakas, and Ioannis Vlahavas, · 2011
Earlier work this paper cites.
“Moving beyond feature design: Deep architectures and automatic feature learning in music informatics.,”
Eric J Humphrey, Juan Pablo Bello, and Yann LeCun, · 2012
Cited alongside, same era.
“Improving neural networks by preventing co-adaptation of feature detectors,”
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov, · 2012
Cited alongside, same era.
“Efficient backprop,”
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller, · 2012
Cited alongside, same era.
“End-to-end learning for music audio,”
Sander Dieleman and Benjamin Schrauwen, · 2014
Later among the works it cites.
“Medleydb: a multitrack dataset for annotation-intensive mir research,”
Rachel Bittner, Justin Salamon, Mike Tierney, Matthias Mauch, Chris Cannam, and Juan Bello, · 2014
Later among the works it cites.
“Speech Acoustic Modeling from Raw Multichannel Waveforms,”
Y. Hoshen, R. J. Weiss, and K. W. Wilson, · 2015
Closest in time.
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