메뉴 건너뛰기




Volumn , Issue , 2014, Pages 53-58

An RNN-based music language model for improving automatic music transcription

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; INFORMATION RETRIEVAL; SPEECH RECOGNITION; STATISTICAL TESTS; TRANSCRIPTION;

EID: 84946061003     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (25)

References (27)
  • 3
    • 84873672132 scopus 로고    scopus 로고
    • A shift-invariant latent variable model for automatic music transcription
    • E. Benetos and S. Dixon. A shift-invariant latent variable model for automatic music transcription. Computer Music Journal, 36(4):81–94, 2012.
    • (2012) Computer Music Journal , vol.36 , Issue.4 , pp. 81-94
    • Benetos, E.1    Dixon, S.2
  • 5
    • 84890543516 scopus 로고    scopus 로고
    • Advances in optimizing recurrent networks
    • May
    • Y. Bengio, N. Boulanger-Lewandowski, and R. Pascanu. Advances in optimizing recurrent networks. In ICASSP, pages 8624–8628, May 2013.
    • (2013) ICASSP , pp. 8624-8628
    • Bengio, Y.1    Boulanger-Lewandowski, N.2    Pascanu, R.3
  • 6
    • 0028392483 scopus 로고
    • Learning long-term dependencies with gradient descent is difficult
    • Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks, 5(2):157–166, 1994.
    • (1994) IEEE Trans. Neural Networks , vol.5 , Issue.2 , pp. 157-166
    • Bengio, Y.1    Simard, P.2    Frasconi, P.3
  • 7
    • 76949083547 scopus 로고    scopus 로고
    • Enforcing harmonicity and smoothness in bayesian non-negative matrix factorization applied to polyphonic music transcription
    • March
    • N. Bertin, R. Badeau, and E. Vincent. Enforcing harmonicity and smoothness in Bayesian non-negative matrix factorization applied to polyphonic music transcription. IEEE Trans. Audio, Speech, and Language Processing, 18(3):538–549, March 2010.
    • (2010) IEEE Trans. Audio, Speech, and Language Processing , vol.18 , Issue.3 , pp. 538-549
    • Bertin, N.1    Badeau, R.2    Vincent, E.3
  • 8
    • 84867593805 scopus 로고    scopus 로고
    • Polyphonic piano note transcription with recurrent neural networks
    • March
    • S. Böck and M. Schedl. Polyphonic piano note transcription with recurrent neural networks. In ICASSP, pages 121–124, March 2012.
    • (2012) ICASSP , pp. 121-124
    • Böck, S.1    Schedl, M.2
  • 9
    • 84867129058 scopus 로고    scopus 로고
    • Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription
    • N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In 29th Int. Conf. Machine Learning, 2012.
    • (2012) 29th Int. Conf. Machine Learning
    • Boulanger-Lewandowski, N.1    Bengio, Y.2    Vincent, P.3
  • 10
    • 84890448143 scopus 로고    scopus 로고
    • High-dimensional sequence transduction
    • May
    • N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent. High-dimensional sequence transduction. In ICASSP, pages 3178–3182, May 2013.
    • (2013) ICASSP , pp. 3178-3182
    • Boulanger-Lewandowski, N.1    Bengio, Y.2    Vincent, P.3
  • 13
    • 77956540787 scopus 로고    scopus 로고
    • Multiple fundamental frequency estimation by modeling spectral peaks and non-peak regions
    • November
    • Z. Duan, B. Pardo, and C. Zhang. Multiple fundamental frequency estimation by modeling spectral peaks and non-peak regions. IEEE Trans. Audio, Speech, and Language Processing, 18(8):2121–2133, November 2010.
    • (2010) IEEE Trans. Audio, Speech, and Language Processing , vol.18 , Issue.8 , pp. 2121-2133
    • Duan, Z.1    Pardo, B.2    Zhang, C.3
  • 14
    • 2442437071 scopus 로고    scopus 로고
    • RWC music database: Music genre database and musical instrument sound database
    • Baltimore, USA, October
    • M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka. RWC music database: music genre database and musical instrument sound database. In ISMIR, Baltimore, USA, October 2003.
    • (2003) ISMIR
    • Goto, M.1    Hashiguchi, H.2    Nishimura, T.3    Oka, R.4
  • 16
    • 85013775361 scopus 로고    scopus 로고
    • Adaptive nonlinear system identification with echo state networks
    • H. Jaeger. Adaptive nonlinear system identification with echo state networks. In Advances in neural information processing systems, pages 593–600, 2002.
    • (2002) Advances in Neural Information Processing Systems , pp. 593-600
    • Jaeger, H.1
  • 18
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • October
    • D. D. Li and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788–791, October 1999.
    • (1999) Nature , vol.401 , pp. 788-791
    • Li, D.D.1    Seung, H.S.2
  • 19
    • 80053451847 scopus 로고    scopus 로고
    • Learning recurrent neural networks with hessian-free optimization
    • J. Martens and I. Sutskever. Learning recurrent neural networks with Hessian-free optimization. In 28th Int. Conf. Machine Learning, pages 1033–1040, 2011.
    • (2011) 28th Int. Conf. Machine Learning , pp. 1033-1040
    • Martens, J.1    Sutskever, I.2
  • 20
    • 84873578548 scopus 로고    scopus 로고
    • A classification-based polyphonic piano transcription approach using learned feature representations
    • October
    • J. Nam, J. Ngiam, H. Lee, and M. Slaney. A classification-based polyphonic piano transcription approach using learned feature representations. In ISMIR, pages 175–180, October 2011.
    • (2011) ISMIR , pp. 175-180
    • Nam, J.1    Ngiam, J.2    Lee, H.3    Slaney, M.4
  • 23
  • 24
    • 77950147390 scopus 로고    scopus 로고
    • Separation by “humming”: User-guided sound extraction from monophonic mixtures
    • October
    • P. Smaragdis and G. Mysore. Separation by “humming”: user-guided sound extraction from monophonic mixtures. In IEEE WASPAA, pages 69–72, October 2009.
    • (2009) IEEE WASPAA , pp. 69-72
    • Smaragdis, P.1    Mysore, G.2
  • 27
    • 0025503558 scopus 로고
    • Backpropagation through time: What it does and how to do it
    • P. J. Werbos. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10):1550–1560, 1990.
    • (1990) Proceedings of the IEEE , vol.78 , Issue.10 , pp. 1550-1560
    • Werbos, P.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.