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seaandsailor

Nov 03, 2014. A really brief introduction to audio signal processing in Julia. Oct 28, 2014. Configuring MATLAB to work with XCode 6 on Yosemite. Apr 27, 2014. Using stochastic neurons for conditional computation. Mar 31, 2014. Using an auditory-inspired representation for speech. Mar 19, 2014. Experiments with a 2 layer MLP incorporating phone information. Feb 25, 2014. Dictionary Learning and Sparse Coding for Speech Signals. Feb 01, 2014. Jan 25, 2014. Jul 28, 2013. Jun 25, 2013.

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seaandsailor | seaandsailor.com Reviews
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Nov 03, 2014. A really brief introduction to audio signal processing in Julia. Oct 28, 2014. Configuring MATLAB to work with XCode 6 on Yosemite. Apr 27, 2014. Using stochastic neurons for conditional computation. Mar 31, 2014. Using an auditory-inspired representation for speech. Mar 19, 2014. Experiments with a 2 layer MLP incorporating phone information. Feb 25, 2014. Dictionary Learning and Sparse Coding for Speech Signals. Feb 01, 2014. Jan 25, 2014. Jul 28, 2013. Jun 25, 2013.
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seaandsailor | seaandsailor.com Reviews

https://seaandsailor.com

Nov 03, 2014. A really brief introduction to audio signal processing in Julia. Oct 28, 2014. Configuring MATLAB to work with XCode 6 on Yosemite. Apr 27, 2014. Using stochastic neurons for conditional computation. Mar 31, 2014. Using an auditory-inspired representation for speech. Mar 19, 2014. Experiments with a 2 layer MLP incorporating phone information. Feb 25, 2014. Dictionary Learning and Sparse Coding for Speech Signals. Feb 01, 2014. Jan 25, 2014. Jul 28, 2013. Jun 25, 2013.

INTERNAL PAGES

seaandsailor.com seaandsailor.com
1

Speech signal representations | seaandsailor

http://www.seaandsailor.com/initial_representation.html

Feb 01, 2014. Our dataset was initially distributed as frame MFCCs (input) and one-hot encoded phonemes (labels). While this representation is usually enough for speech recognition, I believe it is not enough for learning a useful representation for synthesis (as briefly mentioned by Laurent Dinh in his post. Acoustic samples (time domain). Using raw acoustic samples from overlapping frames is the simplest approach. A discrete signal (x[n] ). Is simply a sequence of (real or integer) numbers correspondin...

2

A really brief introduction to audio signal processing in Julia | seaandsailor

http://www.seaandsailor.com/audiosp_julia.html

A really brief introduction to audio signal processing in Julia. Nov 03, 2014. If you are into scientific computing, you probably have already heard about Julia. The magical language that aims to be (almost) as fast as C and as easy as MATLAB and Python to write. I have been playing with Julia for more than a year now, and I really like it and recommend checking it out. Disclaimer: I am one of the developers! And give you something to play with. And the following packages:. Where PackageName is the name ...

3

Experiments with a 2 layer MLP incorporating phone information | seaandsailor

http://www.seaandsailor.com/exp_mlp.html

Experiments with a 2 layer MLP incorporating phone information. Mar 19, 2014. Only the acoustic samples information was used as input. Amjad. Has already done some tests incorporating phone information, but it seems he is using only the current phone. In my experiments, I updated Vincent's. Dataset implementation in order to make it provide the phones corresponding to the current, previous, and next frame. The code can be found in my fork. And the serialized model is here. Frame length: 160 samples.

4

Using stochastic neurons for conditional computation | seaandsailor

http://www.seaandsailor.com/stochastic_neurons_conditional.html

Using stochastic neurons for conditional computation. Apr 27, 2014. As stated in my previous post. The units I used at the output layer are similar to the ones called Stochastic times Smooth (STS) in [Bengio2013]. The idea is that the output of a neuron is equal to the product of a stochastic part (sampled from a binomial distribution with probability ( sqrt{p i} ). And a smooth part (for example, a sigmoid or a linear function). The value of (p i ). Using an undercomplete gammatone dictionary. Reconstru...

5

Resume | seaandsailor

http://www.seaandsailor.com/pages/resume.html

2013-: Ph.D. in Telecommunications at INRS-EMT (Montréal, QC, Canada). Research topic: quality- and environment-aware speech enhancement. 2012-2013: M. Sc. in Telecommunications at INRS-EMT (Montréal, QC, Canada). MSc Thesis Title: A non-intrusive objective speech intelligibility metric tailored for cochlear implant users in complex listening environments. 2005-2011: Electrical Engineering at Universidade Federal de Santa Catarina (Florianópolis, SC, Brazil). Quebec Merit Scholarship for Foreign Students...

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twuilliam.wordpress.com twuilliam.wordpress.com

First experiment — Vanilla MLP with Theano | IFT6266

https://twuilliam.wordpress.com/2014/02/18/first-experiment-vanilla-mlp-with-theano

First experiment — Vanilla MLP with Theano. As a first experiment, I will try to predict the next acoustic sample given a specific number of previous samples. A feedforward neural network (NN) with one hidden layer will be used to perform this task. You will find all my scripts under this repository. To generate the dataset ( generate dataset.py. And train a NN ( mlp.py. The work that I will present is mainly inspired by Hubert. The activation function of the output layer is linear instead of a softmax.

ift6266h14.wordpress.com ift6266h14.wordpress.com

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...

ift6266h14.wordpress.com ift6266h14.wordpress.com

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...

twuilliam.wordpress.com twuilliam.wordpress.com

Last Experiment — Extracting features with SdA | IFT6266

https://twuilliam.wordpress.com/2014/04/30/last-experiment-extracting-features-with-sda/comment-page-1

Last Experiment — Extracting features with SdA. Phonetic information (not working…). I must have a bug in my code. 8230; but time is ticking away, so I decided to come back to my third experiment code (with only the acoustic sample). I will try a stacked denoising auto-encoder (SdA) to see if we can improve the MSE by capturing the distribution of the data. I adapted the code from this tutorial. Ran for around 35 min (SdA MLP) on a GPU. Train: 0.011868, valid 0.056046, test 0.051864. I only ran the exper...

twuilliam.wordpress.com twuilliam.wordpress.com

Last Experiment — Extracting features with SdA | IFT6266

https://twuilliam.wordpress.com/2014/04/30/last-experiment-extracting-features-with-sda

Last Experiment — Extracting features with SdA. Phonetic information (not working…). I must have a bug in my code. 8230; but time is ticking away, so I decided to come back to my third experiment code (with only the acoustic sample). I will try a stacked denoising auto-encoder (SdA) to see if we can improve the MSE by capturing the distribution of the data. I adapted the code from this tutorial. Ran for around 35 min (SdA MLP) on a GPU. Train: 0.011868, valid 0.056046, test 0.051864. I only ran the exper...

twuilliam.wordpress.com twuilliam.wordpress.com

Third experiment — Adding hidden layers | IFT6266

https://twuilliam.wordpress.com/2014/04/29/third-experiment-ahl

Third experiment Adding hidden layers. In this experiment, I will extend my previous post in which I tried to generate a specific phoneme. I made several modifications to my code (you will find the new version in this repository. The main objective was to go beyond the vanilla MLP that I used during my previous experiments by adding hidden layers. The hidden layers are stored in a list, so it is very easy to add layers (see the DBN tutorial for an extensive explanation). The other hyperparameters are:.

twuilliam.wordpress.com twuilliam.wordpress.com

First experiment — Vanilla MLP with Theano | IFT6266

https://twuilliam.wordpress.com/2014/02/18/first-experiment-vanilla-mlp-with-theano/comment-page-1

First experiment — Vanilla MLP with Theano. As a first experiment, I will try to predict the next acoustic sample given a specific number of previous samples. A feedforward neural network (NN) with one hidden layer will be used to perform this task. You will find all my scripts under this repository. To generate the dataset ( generate dataset.py. And train a NN ( mlp.py. The work that I will present is mainly inspired by Hubert. The activation function of the output layer is linear instead of a softmax.

twuilliam.wordpress.com twuilliam.wordpress.com

Some (late) initial thoughts | IFT6266

https://twuilliam.wordpress.com/2014/02/10/some-late-initial-thoughts

Some (late) initial thoughts. This year the project in IFT6266 course will focus on a unified approach to speech synthesis based on deep learning algorithms. Speech synthesis consists of mapping a text to the corresponding acoustic signals. This process is also called a text-to-speech system. According to Wikipedia. These systems first appeared in the late 50’s, which is quite old! As explained by David Krueger. Speech can be defined as an acoustic signal with melody, rhythm and harmony that is why a uni...

twuilliam.wordpress.com twuilliam.wordpress.com

Quick Experiment – Breaking the sine in one line | IFT6266

https://twuilliam.wordpress.com/2014/02/27/quick-experiment-breaking-the-sin-in-one-line/comment-page-1

Quick Experiment – Breaking the sine in one line. As we discussed today in class and as mentioned by Laurent in his post. Adding gaussian noise is important to prevent convergence to a constant. In my last post. I found this kind of behaviour which results in a flatline. Recall Laurent’s post. Is the size of the window. The estimator of the variance is the mean square error (MSE):. Is the number of training examples. Compared to my previous post. Script (go to line 380). To the .wav file):. Second Experi...

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Nov 03, 2014. A really brief introduction to audio signal processing in Julia. Oct 28, 2014. Configuring MATLAB to work with XCode 6 on Yosemite. Apr 27, 2014. Using stochastic neurons for conditional computation. Mar 31, 2014. Using an auditory-inspired representation for speech. Mar 19, 2014. Experiments with a 2 layer MLP incorporating phone information. Feb 25, 2014. Dictionary Learning and Sparse Coding for Speech Signals. Feb 01, 2014. Jan 25, 2014. Jul 28, 2013. Jun 25, 2013.

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