E. Larsen and . Ronald-m-aarts, Audio bandwidth extension: application of psychoacoustics, signal processing and loudspeaker design, 2005.

J. Abel and T. Fingscheidt, Artificial speech bandwidth extension using deep neural networks for wideband spectral envelope estimation, Speech, and Language Processing, vol.26, pp.71-83, 2017.

K. Li and C. Lee, A deep neural network approach to speech bandwidth expansion, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4395-4399, 2015.

Z. Ling, Y. Ai, Y. Gu, and L. Dai, Waveform modeling and generation using hierarchical recurrent neural networks for speech bandwidth extension, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.26, issue.5, pp.883-894, 2018.

A. Gupta, B. Shillingford, Y. Assael, and T. C. Walters, Speech bandwidth extension with wavenet, 2019.

M. Dietz, L. Liljeryd, K. Kjorling, and O. Kunz, Spectral band replication, a novel approach in audio coding, 2002.

P. Ekstrand, Bandwidth extension of audio signals by spectral band replication, Proceedings of the 1st IEEE Benelux Workshop on Model Based Processing and Coding of Audio (MPCA02. Citeseer, 2002.

A. Ehret, . Xd-pan, H. Schug, W. M. Hoerich, . Ren et al., Audio coding technology of exac, Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, pp.290-293, 2004.

T. Friedrich and G. Schuller, Spectral band replication tool for very low delay audio coding applications, 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp.199-202, 2007.

L. James, R. M. Flanagan, and . Golden, Phase vocoder, Bell System Technical Journal, vol.45, issue.9, pp.1493-1509, 1966.

F. Nagel and S. Disch, A harmonic bandwidth extension method for audio codecs, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.145-148, 2009.

L. Dennis, R. Sun, and . Mazumder, Non-negative matrix completion for bandwidth extension: A convex optimization approach, 2013 IEEE International Workshop on Machine Learning for Signal Processing, pp.1-6, 2013.

M. Miron and M. E. Davies, High frequency magnitude spectrogram reconstruction for music mixtures using convolutional autoencoders, Proc. of the 21st Int. Conference on Digital Audio Effects (DAFx-18, pp.173-180, 2018.

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3431-3440, 2015.

F. Yu and V. Koltun, Multi-scale context aggregation by dilated convolutions, 4th International Conference on Learning Representations (ICLR), 2016.

A. Van-den-oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals et al., Wavenet: A generative model for raw audio, 2016.

D. Kingma and J. Ba, Adam: a method for stochastic optimization, 3rd International Conference on Learning Representations (ICLR), 2015.

V. Lostanlen and C. Cella, Deep convolutional networks on the pitch spiral for musical instrument recognition, Proc. of the International Society for Music Information Retrieval Conference, 2016.

G. Tzanetakis and P. Cook, Musical genre classification of audio signals, IEEE Transactions on speech and audio processing, vol.10, issue.5, pp.293-302, 2002.

V. Emiya, E. Vincent, N. Harlander, and V. Hohmann, Subjective and objective quality assessment of audio source separation, IEEE Transactions on Audio, Speech, and Language Processing, vol.19, pp.2046-2057, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00485729

S. Dai, Z. Zhang, and G. G. Xia, Music style transfer: A position paper, 2018.