Jessica Thompson's journal for IFT6266 - Representation Learning at Université de Montréal
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Experiments in deep learning for speech synthesis | Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal | ift6266speechsynthesisjt.wordpress.com Reviews
https://ift6266speechsynthesisjt.wordpress.com
Jessica Thompson's journal for IFT6266 - Representation Learning at Université de Montréal
ift6266speechsynthesisjt.wordpress.com
Noisy samples from Gaussian RBMs | Experiments in deep learning for speech synthesis
https://ift6266speechsynthesisjt.wordpress.com/2014/03/18/noisy-samples-from-gaussian-rbms
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Noisy samples from Gaussian RBMs. March 18, 2014. I’ve continued my tests with the DBM class. I’ve trained a few different models but I’ve been unable to sample or reconstruct anything other than noise. This entry was posted in Uncategorized. Leave a Reply Cancel reply. Enter your comment here. Address never made public).
January | 2014 | Experiments in deep learning for speech synthesis
https://ift6266speechsynthesisjt.wordpress.com/2014/01
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Skip to secondary content. Monthly Archives: January 2014. January 24, 2014. This blog will document my progress on the final project for Dr. Yoshua Bengio’s course on Representation Learning. The task for the project is speech synthesis. January 24, 2014. Washington, DC: U.S. Patent and Trademark Office. November), 1 27. Jaitly, N., a...
February | 2014 | Experiments in deep learning for speech synthesis
https://ift6266speechsynthesisjt.wordpress.com/2014/02
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Skip to secondary content. Monthly Archives: February 2014. Gaussian Visible Units within the pylearn2 DBM package. February 26, 2014. Sutskever, Ilya, Geoffrey Hinton, and GW Taylor. 2008. The Recurrent Temporal Restricted Boltzmann Machine. Neural Information Processing Systems. In the context of predicting the next frame/sample from the...
Representation learning for studying the brain | Experiments in deep learning for speech synthesis
https://ift6266speechsynthesisjt.wordpress.com/2014/04/29/representation-learning-for-studying-the-brain
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Representation learning for studying the brain. April 29, 2014. This entry was posted in Uncategorized. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). You are commenting using your WordPress.com account. ( Log Out. Notify me of new comments via email.
Randomized phases preserves speech content and identity | Experiments in deep learning for speech synthesis
https://ift6266speechsynthesisjt.wordpress.com/2014/03/19/randomized-phases-preserves-speech-content-and-identity
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Randomized phases preserves speech content and identity. March 19, 2014. I decided to make a quick demonstration of the effect of phase information on speech reconstruction. I took the short-time Fourier transform of one of the TIMIT examples ( /TRAIN/DR1/FDAW0/SI1406.wav). This entry was posted in Uncategorized. March 24, 2014 at 7:36 pm.
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ift6266h13yangyang.wordpress.com
Machine Learning on Emotion Recognition | Research Journal of Yangyang Zhao for Machine Learning Course
Machine Learning on Emotion Recognition. Research Journal of Yangyang Zhao for Machine Learning Course. Second attempt with unsupervised pretraining. May 11, 2013. After the previous failure, I change my strategy: I try to train each layer separately, try to find the best models parameter. For the first layer, I try 2304 units gRBM, 576 units gRBM ,. 2304 units DAE, 576 units DAE. For the second layer, I use 576 hidden units gRBM. The minimum construction error is 195.316. 2304 hidden units gRBM layer fo...
Representation Learning - ift6266h14 | Yoshua Bengio's graduate class on representation learning and deep learning
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Web site for the graduate class on Representation Learning Algorithms IFT6266 H14. Instructor: Professor Yoshua Bengio. Teaching assistant: PhD candidate David Warde-Farley. Université de Montréal, département d’informatique et recherche opérationnelle. Course plan ( pdf. Class hours and locations:. Mondays 2:30-4:30pm, Z-210. Thursdays 9:30-11:30am, 1409 PAA. One thought on “ Home. Yoshua B...
IFT6266 – H2015 Representation Learning – A mostly deep learning course
IFT6266 – H2015 Representation Learning. A mostly deep learning course. Project Blogs and Repos. This is a course on representation learning in general and deep learning in particular. Deep learning has recently been responsible for a large number of impressive empirical gains across a wide array of applications including most dramatically in object recognition and detection in images and speech recognition. Département d’informatique et recherche opérationnelle (DIRO). PhD student Vincent Dumoulin.
IFT6266 Project on Representation Learning | A great WordPress.com site
IFT6266 Project on Representation Learning. A great WordPress.com site. Results with ReLUs and different subjects. May 1, 2014. I ran some more experiments based on my previous post:. Using ReLUs on the hidden layers. Using more hidden units in the NSNN. Training/generating with other speakers. 1 Using ReLUs on the hidden layers. NSNN: 2 hidden layers (200 and 150 units). WNN: 1 hidden layer (250 units). Number of epochs: 500. This effect was found to disappear after around 75 epochs. The training proced...
Sina Honari's blog for Representation Learning | My blog on the Representation Learning Course
Sina Honari's blog for Representation Learning. My blog on the Representation Learning Course. Skip to primary content. Skip to secondary content. Fine tuning of unsupervised pretraining. May 11, 2013. Here are the results for the model with GCN preprocessing. In both experiments I had to set the learning rate to 1e-5. The training algorithm diverged using higher values of the learning rates . As the results demonstrate, sigmoid units work better with GCN. May 10, 2013. In the next attempt, I used unsupe...
ift6266speechsynthesisjt.wordpress.com
Experiments in deep learning for speech synthesis | Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal
Experiments in deep learning for speech synthesis. Jessica Thompson's journal for IFT6266 – Representation Learning at Université de Montréal. Skip to primary content. Skip to secondary content. January 24, 2014. This blog will document my progress on the final project for Dr. Yoshua Bengio’s course on Representation Learning. The task for the project is speech synthesis. April 30, 2014. Vincent published a blog post on h ow to use the Pylearn2 TIMIT class with multiple sources, specifically c. Monitor i...
Speech synthesis experiments | Smile! You’re at the best WordPress.com site ever
You’re at the best WordPress.com site ever. The plan, at this point, is to use a gammatone dictionary and sparse coding to train more efficiently on the TIMIT dataset. Gammatone functions are filters applied on frequencies. The goal is to keep frequencies processed by our ears. Joao explains this greatly in his blog. The idea is to use this sparse-coded version as input of a spike and slab RBM segmented by frames of 160 samples and with the previous, current and next phones. I kept the parameters use...
IFT6266 project | Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry
Experiments in speech synthesis with neural networks by Pierre-Luc Vaudry. Predicting the standard deviation. Hubert Banville successfully implemented the kind of architecture I also had in mind. In this experiment, I wanted to see if having this standard deviation predicted by the neural network could improve the results. I based myself on Hubert’s code, which is described in his last post. To this loss function, the L1 and L2 norm penalties are also added as regulators, as in Hubert’s experiments...
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