Corpus ID: 26806839

Audio Deepdream: Optimizing raw audio with convolutional networks

@inproceedings{Roberts2016AudioDO,
  title={Audio Deepdream: Optimizing raw audio with convolutional networks},
  author={Adam Roberts and Cinjon Resnick and Diego Ardila and D. Eck},
  year={2016}
}
The hallucinatory images of DeepDream [8] opened up the floodgates for a recent wave of artwork generated by neural networks. [...] Key Method Consequently, we have followed in the footsteps of Van den Oord et al [13] and trained a network to predict embeddings that were themselves the result of a collaborative filtering model. A key difference is that we learn features directly from the raw audio, which creates a chain of differentiable functions from raw audio to high level features.Expand
Learning Hierarchy Aware Embedding From Raw Audio for Acoustic Scene Classification
  • V. Abrol, P. Sharma
  • Computer Science
  • IEEE/ACM Transactions on Audio, Speech, and Language Processing
  • 2020
On Using Backpropagation for Speech Texture Generation and Voice Conversion
A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor
  • D. Sheng, György Fazekas
  • Computer Science, Engineering
  • 2019 International Joint Conference on Neural Networks (IJCNN)
  • 2019
5-30-2018 Generating Audio Using Recurrent Neural Networks

References

SHOWING 1-10 OF 14 REFERENCES
Going deeper with convolutions
End-to-end learning for music audio
  • S. Dieleman, B. Schrauwen
  • Computer Science
  • 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2014
Speech acoustic modeling from raw multichannel waveforms
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
A Neural Algorithm of Artistic Style
Deep content-based music recommendation
...
1
2
...