Improving music source separation based on deep neural networks through data augmentation and network blending

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2017, Pages 261-265

  Uhlich, Stefan ^a   Porcu, Marcello ^a   Giron, Franck ^a   Enenkl, Michael ^a   Kemp, Thomas ^a   Takahashi, Naoya ^b   Mitsufuji, Yuki ^b  

^a Sony Deutschland GmbH   (Germany)

^b SONY CORPORATION   (Japan)

Author keywords

Blending; Deep neural network (DNN); Long short term memory (LSTM); Music source separation (MSS)

Indexed keywords

EID: 85023774072     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2017.7952158     Document Type: Conference Paper

References (43)

1. Z. Rafii and B. Pardo, "Repeating pattern extraction technique (REPET): A simple method for music/voice separation," IEEE Trans, on Audio, Speech, and Language Processing, Vol. 21, no. 1, pp. 73-84, 2013.
2. J.-L. Durrieu, B. David, and G. Richard, "A musically motivated mid-level representation for pitch estimation and musical audio source separation," IEEE Journal on Selected Topics on Signal Processing, Vol. 5, pp. 1180-1191, 2011.
3. H. Shim, J. S. Abel, and K.-M. Sung, "Stereo music source separation for 3-D upmixing," in 127th AES Convention, 2009.
4. D. FitzGerald, "Upmixing from mono - a source separation approach," Proc. Digital Signal Processing, 2011.
5. D. FitzGerald, "The good vibrations problem," 134th AES Convention, e-brief, 2013.
6. "SiSEC MUS Homepage," https://sisec.inria.fr/home/2016-professionally-produced-music-recordings/.
7. N. Ono, Z. Rafii, D. Kitamura, N. Ito, and A. Liutkus, "The 2015 signal separation evaluation campaign," in Proc. LVA/ICA, 2015, pp. 387-395.
8. A. A. Nugraha, A. Liutkus, and E. Vincent, "Multichannel audio source separation with deep neural networks," INRIA Technical Report, 2015.
9. A. A. Nugraha, A. Liutkus, and E. Vincent, "Multichannel music separation with deep neural networks," in Proc. EUSIPCO, 2016.
10. S. Uhlich, F. Giron, and Y. Mitsufuji, "Deep neural network based instrument extraction from music," in Proc. ICASSP, 2015, pp. 2135-2139.
11. Y. LeCun, Y Bengio, and G. Hinton, "Deep learning," Nature, Vol. 521, no. 7553, pp. 436-444, 2015.
12. E. M. Grais, M. U. Sen, and H. Erdogan, "Deep neural networks for single channel source separation," Proc. IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3734-3738, 2014.
13. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, "Singing-voice separation from monaural recordings using deep recurrent neural networks.," in Proc. ISMIR, 2014, pp. 477-482.
14. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, "Joint optimization of masks and deep recurrent neural networks for monaural source separation," IEEE/ACM Trans, on Audio, Speech, and Language Processing, Vol. 23, no. 12, pp. 2136-2147, 2015.
15. A. J. Simpson, G. Roma, and M. D. Plumbley, "Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network," in Proc. LVA/ICA, 2015, pp. 429-436.
16. J. Le Roux, S. Watanabe, and J. R. Hershey, "Ensemble learning for speech enhancement," in Proc. WASPAA, 2013, pp. 1-4.
17. X. Jaureguiberry, G. Richard, P. Leveau, R. Hennequin, and E. Vincent, "Introducing a simple fusion framework for audio source separation," in Proc. MLSP, 2013, pp. 1-6.
18. X. Jaureguiberry, E. Vincent, and G. Richard, "Fusion methods for speech enhancement and audio source separation," IEEE/ACM Trans, on Audio, Speech, and Language Processing, Vol. 24, no. 7, pp. 1266-1279, 2016.
19. E. M. Grais, G. Roma, A. J. R. Simpson, and M. D. Plumbley, "Combining mask estimates for single channel audio source separation using deep neural networks," in Proc. Interspeech, 2016.
20. E. M. Grais, G. Roma, A. J. R. Simpson, and M. D. Plumbley, "Single-channel audio source separation using deep neural network ensembles," in 140th AES Convention, 2016.
21. X.-L. Zhang and D. Wang, "Multi-resolution stacking for speech separation based on boosted DNN," in Proc. Inter-speech, 2015, pp. 1745-1749.
22. X.-L. Zhang and D. Wang, "A deep ensemble learning method for monaural speech separation," IEEE/ACM Trans, on Audio, Speech, and Language Processing, Vol. 24, no. 5, pp. 967-977, 2016.
23. W Jiang, S. Liang, L. Dong, H. Yang, W Liu, and Y Wang, "Cross-domain cooperative deep stacking network for speech separation," in Proc. ICASSP. IEEE, 2015, pp. 5083-5087.
24. J. Eggert and E. Körner, "Sparse coding and NMF," in Proc. Neural Networks, 2004, vol. 4, pp. 2529-2533.
25. C. Févotte, N. Bertin, and J.-L. Durrieu, "Nonnegative matrix factorization with the Itakura-Saito divergence: With application to music analysis," Neural computation, Vol. 21, no. 3, pp. 793-830, 2009.
26. F. Weninger, J. Le Roux, J. R. Hershey, and S. Watanabe, "Discriminative NMF and its application to single-channel source separation," in Proc. Interspeech, 2014, pp. 865-869.
27. J. Le Roux, J. R. Hershey, and F. Weninger, "Deep NMF for speech separation," in Proc. ICASSP, 2015, pp. 66-70.
28. E. Vincent, R. Gribonval, and C. Févotte, "Performance measurement in blind audio source separation," IEEE Trans, on Audio, Speech and Language Processing, Vol. 14, no. 4, pp. 1462-1469, 2006.
29. N. Q. Duong, E. Vincent, and R. Gribonval, "Under-determined reverberant audio source separation using a full-rank spatial covariance model," IEEE Trans, on Audio, Speech, and Language Processing, Vol. 18, no. 7, pp. 1830-1840, 2010.
30. A. Ozerov, E. Vincent, and F. Bimbot, "A general flexible framework for the handling of prior information in audio source separation," IEEE Trans, on Audio, Speech, and Language Processing, Vol. 20, no. 4, pp. 1118-1133, 2012.
31. S.M. Kay, Fundamentals oI'Statistical Signal Processing, Volume 1: Estimation Theory, Prentice-Hall, 1993.
32. S. Sivasankaran, A. A. Nugraha, E. Vincent, J. A. Morales-Cordovilla, S. Dalmia, I. Illina, and A. Liutkus, "Robust ASR using neural network based speech enhancement and feature simulation," in Proc. ASRU, 2015, pp. 482-489.
33. X. Glorot, A. Bordes, and Y Bengio, "Deep sparse rectifier networks," Proc. AISTATS, Vol. 15, pp. 315-323, 2011.
34. R. M. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam, and J. P. Bello, "MedleyDB: A multitrack dataset for annotation-intensive MIR research.," in Proc. ISMIR, 2014, pp. 155-160.
35. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification and Scene Analysis, John Wiley & Sons, 2001.
36. A. Graves and J. Schmidhuber, "Framewise phoneme classification with bidirectional LSTM and other neural network architectures," Neural Networks, Vol. 18, no. 5, pp. 602-610, 2005.
37. "Lasagne GitHub," https://github.com/Lasagne/Lasagne.
38. "Theano GitHub," https://github.com/Theano/Theano.
39. The Theano Development Team, "Theano: A python framework for fast computation of mathematical expressions," arXiv preprintarXiv: 1605.02688, 2016.
40. J. Schlüter and T. Grill, "Exploring data augmentation for improved singing voice detection with neural networks," in Proc. ISMIR, 2015.
41. B. McFee, E. J. Humphrey, and J. P. Bello, "A software framework for musical data augmentation," in Proc. ISMIR, 2015.
42. J. Bennett and S. Lanning, "The Netflix prize," in Proc. KDD Cup and Workshop, 2007, vol. 2007, p. 35.
43. R. M. Bell and Y Koren, "Lessons from the Netflix prize challenge," ACM SIGKDD Explorations Newsletter, Vol. 9, no. 2, pp. 75-79, 2007.