Diagonal rnns in symbolic music modeling

Y. Cem Subakan, Paris Smaragdis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we use Diagonal Recurrent Neural Networks on a sequence prediction task. The modification from standard RNN is simple: Diagonal recurrent matrices are used instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of our experiments. We show the benefits of using diagonal recurrent matrices with popularly used LSTM and GRU architectures as well as with the vanilla RNN architecture, on four standard symbolic music datasets.

Original languageEnglish (US)
Title of host publication2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages354-358
Number of pages5
ISBN (Electronic)9781538616321
DOIs
StatePublished - Dec 7 2017
Externally publishedYes
Event2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 - New Paltz, United States
Duration: Oct 15 2017Oct 18 2017

Publication series

NameIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Volume2017-October

Other

Other2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
CountryUnited States
CityNew Paltz
Period10/15/1710/18/17

Keywords

  • Recurrent Neural Networks
  • Symbolic Music Modeling

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications

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