E. Pollastri, A pitch tracking system dedicated to process singing voice for music retrieval, Multimedia and Expo, 2002. ICME'02. Proceedings. 2002 IEEE International Conference on, vol.1, pp.341-344, 2002.

A. Mesaros, T. Virtanen, and A. Klapuri, Singer identification in polyphonic music using vocal separation and pattern recognition methods.," in ISMIR, pp.375-378, 2007.

A. Mesaros, Singing voice recognition for music information retrieval, Tampereen teknillinen yliopisto. Julkaisu-Tampere University of Technology. Publication, vol.1064, 2012.

F. Stöter, A. Liutkus, and N. Ito, The 2018 signal separation evaluation campaign, International Conference on Latent Variable Analysis and Signal Separation, pp.293-305, 2018.

A. Jansson, J. Eric, N. Humphrey, R. Montecchio, A. Bittner et al., Singing voice separation with deep u-net convolutional networks, Proceedings of the International Society for Music Information Retrieval Conference, pp.323-332, 2017.

N. Takahashi and Y. Mitsufuji, Multi-scale multi-band densenets for audio source separation, Applications of Signal Processing to Audio and Acoustics (WASPAA, pp.21-25, 2017.

D. Stoller, S. Ewert, and S. Dixon, Wave-unet: A multi-scale neural network for end-to-end audio source separation, 2018.

S. Uhlich, M. Porcu, F. Giron, M. Enenkl, T. Kemp et al., Improving music source separation based on deep neural networks through data augmentation and network blending, Acoustics, Speech and Signal Processing, p.2017

, IEEE, pp.261-265, 2017.

D. Stoller, S. Ewert, and S. Dixon, Adversarial semi-supervised audio source separation applied to singing voice extraction, 2017.

N. Takahashi, N. Goswami, and Y. Mitsufuji, Mmdenselstm: An efficient combination of convolutional and recurrent neural networks for audio source separation, 2018.

A. A. Nugraha, A. Liutkus, and E. Vincent, Multichannel music separation with deep neural networks, Signal Processing Conference (EUSIPCO), pp.1748-1752, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01334614

Y. Luo, Z. Chen, R. John, J. L. Hershey, N. Roux et al., Deep clustering and conventional networks for music separation: Stronger together, Acoustics, Speech and Signal Processing, pp.61-65, 2017.

Y. Zhe-cheng-fan, J. Lai, and . Jang, Svsgan: Singing voice separation via generative adversarial network, 2017.

P. Chandna, M. Miron, J. Janer, and E. Gómez, Monoaural audio source separation using deep convolutional neural networks, International Conference on Latent Variable Analysis and Signal Separation, pp.258-266, 2017.

K. Stylianos-ioannis-mimilakis, T. Drossos, G. Virtanen, and . Schuller, A recurrent encoder-decoder approach with skip-filtering connections for monaural singing voice separation, vol.1709, 2017.

J. R. Andrew, G. Simpson, M. D. Roma, and . Plumbley, Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network, International Conference on Latent Variable Analysis and Signal Separation, pp.429-436, 2015.

L. Chao, J. Hsu, and . Jang, On the improvement of singing voice separation for monaural recordings using the mir-1k dataset, IEEE Transactions on Audio, Speech, and Language Processing, vol.18, issue.2, pp.310-319, 2010.

M. Rachel, J. Bittner, M. Salamon, M. Tierney, C. Mauch et al., Medleydb: A multitrack dataset for annotation-intensive mir research, ISMIR, vol.14, pp.155-160, 2014.

A. Liutkus, F. Stöter, Z. Rafii, D. Kitamura, B. Rivet et al., The 2016 signal separation evaluation campaign, Latent Variable Analysis and Signal Separation -12th International Conference, LVA/ICA 2015, pp.323-332, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01472932

Z. Rafii, A. Liutkus, and F. Stöter, The MUSDB18 corpus for music separation, Stylianos Ioannis Mimilakis, and Rachel Bittner, 2017.

E. Humphrey, N. Montecchio, R. Bittner, A. Jansson, and T. Jehan, Mining labeled data from webscale collections for vocal activity detection in music, Proceedings of the 18th ISMIR Conference, 2017.

J. Schlüter, Deep Learning for Event Detection, Sequence Labelling and Similarity Estimation in Music Signals, 2017.

A. Flexer, A closer look on artist filters for musical genre classification, World, vol.19, issue.122, pp.16-17, 2007.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.