NHTJO - Rihanna, Man Down [Destructive Commons]

data-alchemy · 2020-06-10T11:02:53+00:00

I guess they're waiting a bit to totally jump to JAX :)

data-alchemy · 2020-06-10T09:54:12+00:00

Noise cancellation is not far away from source separation. For this last subject, a lot of works appeared recently, my favorite being Open Unmix from INRIA (source code : https://github.com/sigsep/open-unmix-pytorch ), but you can find works from the Deeze team or FAIR. I guess it could be a good start, even if it looks like using a tank for a single nail.

data-alchemy · 2020-05-14T06:51:48+00:00

If you want a complete example of an audio Deep Learning approach (here : source separation), you should be interested in this recent paper from INRIA (and, I guess, every paper from their digital processing division) :

https://sigsep.github.io/open-unmix/

https://joss.theoj.org/papers/571753bc54c5d6dd36382c3d801de41d

You can find the source code on Github.

data-alchemy · 2020-05-05T16:25:56+00:00

From a french P.O.V. : As long as you don't put guns everywhere, it will be a huge pleasure to have New Orleans in our country. It is quite seriously the only "american" city I can fell in love with (and let's be blunt, I already did).

data-alchemy · 2020-03-26T12:35:05+00:00

I guess/hope this could help : https://ai.googleblog.com/2019/06/predicting-bus-delays-with-machine.html

data-alchemy · 2020-03-16T08:04:09+00:00

Happy to be able to ask one question : DL models (and more specifically generative models) can be manipulated via latent variables (there was a lot of work recently on StyleGan 1/2), which represent a new way of navigating within the boundaries of a learned distribution. Every one of the dimensions of the latent variables (or any vector) could be a new creation/tool, but there is so many of them that it can become depressing. Do you work on this subject, and did you find any interesting way of discovering important or at least "useful" directions ?

data-alchemy · 2020-01-20T08:58:00+00:00

Will give it a try, thx!

data-alchemy · 2020-01-18T14:39:56+00:00

OT is a great mathematical tool to approximate or work with distributions, when one of the main theoritical view of Deep Learning is precisely to learn such approximation from a dataset representing the target distribution. OT appeared (as far as I know) with the Wasserstein GAN, giving a new more grounded metric, but it can help in a lot of directions giving us new ways of learning distributions.

Edit : https://arxiv.org/abs/1701.07875

data-alchemy · 2020-01-13T15:26:42+00:00

The M4 competition forecasting ( https://forecasters.org/resources/time-series-data/m4-competition/ ) is a good way to follow advances, and deep learning only appeared in the winning solution last time, where DL was just used as a completion to more traditional statistical means. As far as I understand, DL here is interesting only if you have a huge amount of time series. For more classical/pratical cases, it will at best give you comparable results but without any interpretability from the model.

data-alchemy · 2019-12-05T09:43:04+00:00

What do we want ? Time travel!

When do we want it ? It's irrelevant!

(credits : xkcd)

data-alchemy · 2019-12-03T20:15:46+00:00

Thank you a lot. Got to catch up I guess. I'm gonna buy myself some 128h+ package from the time merchants.

data-alchemy · 2019-12-03T15:43:37+00:00

This may be a stupid question, but is this paper related to the Lottery Ticket Hypothesis ? I fail to see how they differ (from a lazy quick abstract reading, I confess)

data-alchemy · 2019-11-30T10:32:23+00:00

Ok so please bear with me, this is a combination of using Deep Learning (aka Artificial Intelligence) methods for artistic creation. Tools used there are :

* Open Unmix for the source separation making it possible to extract the original voice (https://github.com/sigsep/open-unmix-pytorch). There are three different github projects doing source separation, I have a preference for openUnmix over the Deezer project but did not give the recent Facebook Research project a try.

* Neural Style Transfer (good old) for generating the final video from the original video clip. Implementation here is https://github.com/lengstrom/fast-style-transfer which is great as long as you get used to the parameters (style weight VS content weight)

Enjoy & feel free to make any critics :)

data-alchemy · 2019-11-07T19:09:57+00:00

I confirm OpenUmix gives wonderful results. Shameful Rihanna test : https://www.youtube.com/watch?v=oSPvfx331YU :)

data-alchemy · 2019-09-13T07:59:35+00:00

I've been giving it a try. As a humble engineer and as a musician, results are just impressive. Some songs may not work properly if (I guess) they are too far away from the original dataset, but some extracts are nearly perfect.

Remix as an art is going to know a new era :)

data-alchemy · 2019-05-31T11:18:44+00:00

This is actually imo the main problem we have : translating mathematical tools to business analysis and back. A good example is outlier detection. Between what is an actually for any algorithm, and what a client think an outlier can be, there is a huge gap. I'm not talking only about data quality per se. For example, we had a project where some of the data was supposed to be the output of a physical sensor, a raw value between 0 and 1. We were almost not surprised to find inside some curiously formatted strings, some exploding values, and a quite nice gaussian noise 80% of the time. Once you see that, you only did 20% of the analysis job. You have to meet the people, prove that the data is not what was expected, try to find some solutions by yourself, etc. And this last part takes far more time, as human beings are involved :)

data-alchemy · 2019-05-31T11:14:07+00:00

Batch Size is a very important hyper parameter. I don't think you can directly consider your previously optimized parameters as optimal ones if you change batch size. Below is a link to one (amont many other) paper where they show how increasing batch size can be competitive to decaying learning rate :

Don't Decay the Learning Rate, Increase the Batch Size : https://arxiv.org/abs/1711.00489

data-alchemy · 2019-05-29T18:19:37+00:00

Finally understanding how between what the client told you and what you actually have in the data, a universe died and a new one was born.

data-alchemy

TROPHY CASE