%0 Generic %1 mehri2016samplernn %A Mehri, Soroush %A Kumar, Kundan %A Gulrajani, Ishaan %A Kumar, Rithesh %A Jain, Shubham %A Sotelo, Jose %A Courville, Aaron %A Bengio, Yoshua %D 2016 %K dnn rnn speech speech-synthesis %T SampleRNN: An Unconditional End-to-End Neural Audio Generation Model %U http://arxiv.org/abs/1612.07837 %X In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.

@misc{mehri2016samplernn, abstract = {In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.}, added-at = {2018-10-01T15:29:35.000+0200}, author = {Mehri, Soroush and Kumar, Kundan and Gulrajani, Ishaan and Kumar, Rithesh and Jain, Shubham and Sotelo, Jose and Courville, Aaron and Bengio, Yoshua}, biburl = {https://www.bibsonomy.org/bibtex/25bc72d74bd81f571b1b08a0c699e5c03/geistgesicht}, description = {[1612.07837] SampleRNN: An Unconditional End-to-End Neural Audio Generation Model}, interhash = {3b4bfced373e7a8516696b8627f3ecbc}, intrahash = {5bc72d74bd81f571b1b08a0c699e5c03}, keywords = {dnn rnn speech speech-synthesis}, note = {cite arxiv:1612.07837}, timestamp = {2018-10-01T15:29:35.000+0200}, title = {SampleRNN: An Unconditional End-to-End Neural Audio Generation Model}, url = {http://arxiv.org/abs/1612.07837}, year = 2016 }