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IFT6266 Project : Speech Synthesis – Benjamin AubertBenjamin Aubert
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IFT6266 Project : Speech Synthesis – Benjamin Aubert | benjaubert.wordpress.com Reviews
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Benjamin Aubert
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Benjamin – IFT6266 Project : Speech Synthesis
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IFT6266 Project : Speech Synthesis. April 30, 2014. 7) Final Training with Stacked DAE. The batch is still running…. I will post my last results when it will be finish ( Epoch 591 / 5000 ). I’m trying to encode a frame of 560 samples (phoneme ‘oe’) in order to have a multidimensionality reduction of a factor ten. The architecture is a stack of three DEA: 560-Inputs 560-Outputs / 560-Inputs 100-Outputs / 100-Inputs 50-Outputs. April 28, 2014. 6) Signal reconstruction based on a single auto-encoder level.
February 2014 – IFT6266 Project : Speech Synthesis
https://benjaubert.wordpress.com/2014/02
IFT6266 Project : Speech Synthesis. February 12, 2014. 3) Linear Prediction Coefficient (LPC). In speech synthesis, a big issue is the generation of a continuous acoustic signal. We want to predict the next acoustic samples from previous samples (a). As first estimate, a LPC linear model can be used. It aims to find coefficients in order to minimize equation (b) :. With Yt the sample at time t, K the number of samples in the window that precede Yt. As in my post #2. February 10, 2014. My test code was.
IFT6266 Project : Speech Synthesis – Page 2 – Benjamin Aubert
https://benjaubert.wordpress.com/page/2
IFT6266 Project : Speech Synthesis. February 1, 2014. Speech Synthesis Project from TIMIT dataset. The project is the. Module (Representation Learning using Deep Learning) at Univ. De Montréal. Dataset provides acoustic samples of voices (english speakers) labelled with corresponding word and phonemes. Designed for speech recognition, we will use here the dataset to perform Speech Synthesis. Speech synthesis rises two majors issues:. January 23, 2014. Newer posts →. 7) Final Training with Stacked DAE.
2) First test with PyLearn2 – IFT6266 Project : Speech Synthesis
https://benjaubert.wordpress.com/2014/02/10/2-first-test-with-pylearn2/comment-page-1
IFT6266 Project : Speech Synthesis. February 10, 2014. 2) First test with PyLearn2. In order to begin, the idea is to construct a simple linear MLP that predict the next scalar sample from acoustic samples that precede (given a fixed window). My test code was. And carried out the following steps:. 8211; Read an acoustic sample (WAV file as numpy array). 8211; Normalize data between [-1 1] with zero mean ( if no voice, value was zero). X: an array of diemensions [N 100]. Train/Test Data size: 31694 each.
3) Linear Prediction Coefficient (LPC) – IFT6266 Project : Speech Synthesis
https://benjaubert.wordpress.com/2014/02/12/3-linear-prediction-coefficient-lpc
IFT6266 Project : Speech Synthesis. February 12, 2014. 3) Linear Prediction Coefficient (LPC). In speech synthesis, a big issue is the generation of a continuous acoustic signal. We want to predict the next acoustic samples from previous samples (a). As first estimate, a LPC linear model can be used. It aims to find coefficients in order to minimize equation (b) :. With Yt the sample at time t, K the number of samples in the window that precede Yt. As in my post #2. 2) First test with PyLearn2. 5) Auto-e...
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Apr10 – Exam | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/2014/04/03/apr10-exam/comment-page-1
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Apparently successful results with RNNs →. Apr10 – Exam. Posted by Yoshua Bengio. Please prepare for the final exam by reviewing the material studied during the term and the question/answers produced by all of us. Examples of exams from previous years can be found there. 15 thoughts on “ Apr10 – Exam. April 6, 2014 at 17:57. April 6, 2014 at 21:07. April 7, 2014 at 17:41. Http:/ learninglear...
Apr10 – Exam | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/2014/04/03/apr10-exam
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Apparently successful results with RNNs →. Apr10 – Exam. Posted by Yoshua Bengio. Please prepare for the final exam by reviewing the material studied during the term and the question/answers produced by all of us. Examples of exams from previous years can be found there. 15 thoughts on “ Apr10 – Exam. April 6, 2014 at 17:57. April 6, 2014 at 21:07. April 7, 2014 at 17:41. Http:/ learninglear...
Annotated waveforms | DAVIDTOB
https://davidtob.wordpress.com/2014/02/05/annotated-waveforms
This blog records my experiments for the course ift6266h14 at Université de Montréal. February 5, 2014. I made a basic visualization of the sentence data (similar to this. If you haven’t looked at the data yet, this should make it clear what it contains. Each coloured area is a time interval tagged with a certain transcription symbol, each bar like - is a time interval tagged with a word. This recording can be downloaded from the public sample. So I guess it’s ok to upload it here).
Mar13 | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/2014/03/11/mar13/comment-page-1
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Final Exam →. Posted by Yoshua Bengio. Please study the following material in preparation for the March 13th class:. Of book draft on Deep Learning. Directory includes bibliography) by Y. Bengio, I. Goodfellow and A. Courville. Please put up your questions below, as replies to this post. 33 thoughts on “ Mar13. March 11, 2014 at 10:03. March 11, 2014 at 11:10. March 11, 2014 at 23:39. You ca...
Mar27 | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/2014/03/23/mar27
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Posted by Yoshua Bengio. Please study the following material (on auto-encoders) in preparation for the March 27th class:. Section 7 of Representation Learning: A Review and New Perspectives. By Y Bengio, A. Courville and P. Vincent. Videos 6.1 to 6.7 (Auto-encoders) from Hugo Larochelle’s course on neural networks. Please put up your questions below, as replies to this post. 1 what is it?
Resources | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/resources
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Geoff Hinton’s course on Neural Networks for Machine Learning. Hugo Larochelle’s videos. On neural networks and deep learning. YB’s paper on Practical recommendations for gradient-based training of deep architectures. Chapters of the upcoming Deep Learning book. Including list of references). YB’s 2009 book on Learning deep architectures for AI. By YB, Aaron Courville and Pascal Vincent.
Apr3 | Representation Learning - ift6266h14
https://ift6266h14.wordpress.com/2014/03/31/apr3
Representation Learning – ift6266h14. Yoshua Bengio's graduate class on representation learning and deep learning. Apr10 – Exam →. Posted by Yoshua Bengio. Please study the following material (on auto-encoders) in preparation for the April 3rd class:. Better mixing via deep representations. ICML’2013) by Y. Bengio, Grégoire Mesnil, Yann Dauphin and Salah Rifai. Generalized denoising auto-encoders as generative models. NIPS’2013) by Y. Bengio, Yao Li, Guillaume Alain and Pascal Vincent. Disentangling give...
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IFT6266 Project : Speech Synthesis – Benjamin Aubert
IFT6266 Project : Speech Synthesis. April 30, 2014. 7) Final Training with Stacked DAE. The batch is still running…. I will post my last results when it will be finish ( Epoch 591 / 5000 ). I’m trying to encode a frame of 560 samples (phoneme ‘oe’) in order to have a multidimensionality reduction of a factor ten. The architecture is a stack of three DEA: 560-Inputs 560-Outputs / 560-Inputs 100-Outputs / 100-Inputs 50-Outputs. April 28, 2014. 6) Signal reconstruction based on a single auto-encoder level.
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