WaveNet

WaveNet: A Generative Model for Raw Audio. This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.


References in zbMATH (referenced in 26 articles )

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  1. Dash, Tirtharaj; Srinivasan, Ashwin; Baskar, A.: Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (2022)
  2. Kilgour, Michael; Simine, Lena: Inside the black box: a physical basis for the effectiveness of deep generative models of amorphous materials (2022)
  3. Marino, Joseph: Predictive coding, variational autoencoders, and biological connections (2022)
  4. Wang, Jia-Ji; Wang, Chen; Fan, Jian-Sheng; Mo, Y. L.: A deep learning framework for constitutive modeling based on temporal convolutional network (2022)
  5. Chatigny, Philippe; Patenaude, Jean-Marc; Wang, Shengrui: Spatiotemporal adaptive neural network for long-term forecasting of financial time series (2021)
  6. Evans, Richard; BoĆĄnjak, Matko; Buesing, Lars; Ellis, Kevin; Pfau, David; Kohli, Pushmeet; Sergot, Marek: Making sense of raw input (2021)
  7. Girin, Laurent; Leglaive, Simon; Bie, Xiaoyu; Diard, Julien; Hueber, Thomas; Alameda-Pineda, Xavier: Dynamical variational autoencoders: a comprehensive review (2021)
  8. Huang, Junhao; Sun, Weize; Huang, Lei: Joint structure and parameter optimization of multiobjective sparse neural network (2021)
  9. Ma, Shaohui; Fildes, Robert: Retail sales forecasting with meta-learning (2021)
  10. Paul, William; Wang, I-Jeng; Alajaji, Fady; Burlina, Philippe: Unsupervised discovery, control, and disentanglement of semantic attributes with applications to anomaly detection (2021)
  11. Song, Suihong; Mukerji, Tapan; Hou, Jiagen: Geological facies modeling based on progressive growing of generative adversarial networks (GANs) (2021)
  12. Song, Suihong; Mukerji, Tapan; Hou, Jiagen: GANSim: conditional facies simulation using an improved progressive growing of generative adversarial networks (GANs) (2021)
  13. Vlassis, Nikolaos N.; Sun, WaiChing: Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (2021)
  14. Wang, Zhengyang; Ji, Shuiwang: Smoothed dilated convolutions for improved dense prediction (2021)
  15. Wan, Zhong Yi; Dodov, Boyko; Lessig, Christian; Dijkstra, Henk; Sapsis, Themistoklis P.: A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets (2021)
  16. Brehmer, Johann; Louppe, Gilles; Pavez, Juan; Cranmer, Kyle: Mining gold from implicit models to improve likelihood-free inference (2020)
  17. Gatti, Filippo; Clouteau, Didier: Towards blending physics-based numerical simulations and seismic databases using generative adversarial network (2020)
  18. Pavllo, Dario; Feichtenhofer, Christoph; Auli, Michael; Grangier, David: Modeling human motion with quaternion-based neural networks (2020)
  19. Sangiorgio, Matteo; Dercole, Fabio: Robustness of LSTM neural networks for multi-step forecasting of chaotic time series (2020)
  20. Wiese, Magnus; Knobloch, Robert; Korn, Ralf; Kretschmer, Peter: Quant GANs: deep generation of financial time series (2020)

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