That's pretty cool. Just for the fun of it, I also did something along these lines, trying to make a model of human voice with stacked denoising autoencoders (S

That's pretty cool. Just for the fun of it, I also did something along these lines, trying to make a model of human voice with stacked denoising autoencoders (SDAs, which were used in the image recognition originally) in the compressed audio domain (MDCT, basically MP3, but without Huffman encoding). It worked, but a simple nearest neighbour model was producing a lot more realistic voice. Here's how audio data in that compressed domain looks like [you'll need to zoom in]: https://raw.github.com/dchichkov/DeepLearningTutorials/master/code/plots/hhgttg0101007.png https://raw.github.com/dchichkov/DeepLearningTutorials/maste...