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Month: June 2017

Going Cold Brew – Sweet and Smooth Sensations

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I must admit, I feel a bit foolish having never tried cold brewed coffee. Last week it was recommended to me. So, I ground up one of my daily favorite beans, Philz – Jacobs Wonderbar and prepared my first cold-brew. I was astonished in two ways.

1. Wow was it smooth and sweet.

2. It literally tastes like a different bean, and this is a bean I know well.

So, after consuming my wonderful Wonderbar beans I moved on to another favorite, Pete’s Major Dickason’s Blend®. Again, astonished how much sweeter it was, absolutely zero bitterness and like a totally different coffee.

I use a Bodum 34oz french press. For Jacobs Wonderbar I grind my bean medium to medium fine with a ratio of 60 grams (weighed on a scale) to about 34oz of water (the Bodum container isn’t completely full).

I slowly add cold filtered water, via a cup, used as a transfer device; study Integral Principles of the Structural Dynamics of Flow by Leslie Claret for this process, from my refrigerator and carefully stir with a spoon or chopstick. This is usually occurring between 8 and 10PM at night.

When I wake in the morning I plunge the French press, pour it into my coffee cup and nuke it in the microwave for 2 minutes. I use a big coffee cup, so 2 minutes isn’t scorching, but I do still like my coffee warm.

I’m looking forward to trying many more of my favorite beans as it seems it’s like a new whole world of taste sensations! My buddy Mike Terry, whom I often discuss such endeavors with, recommends I now try “Nitro-brewing”. I’ll have to look into that…

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Deepmind’s WaveNet Composes Original Piano Piece

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“WaveNets” Borrowed from WaveNet

   

WaveNet by Deepmind and/or Alphabet, Inc. and/or Google has created a very impressive audio signal modeling system.  Although Adobe’s Voco is cool, my feeling is WaveNet is much further along in development, it’s just missing the flashy live video demonstration that went viral from Adobe.

A quick summary of what WaveNet is capable of.

They trained WaveNet on a dataset of classical piano music. This dataset was generated by modeling the raw waveforms of the classical music pieces, one sample at a time with approximately 16,000 samples per second. They did not provide a musical score, structured sequence, or order, they simply allowed WaveNet to generate it’s own musical compositions based on what it learned from the music dataset. Then WaveNet takes all those many thousands of tiny audio soundwave samples and reconstructs it own compositions. Really quite amazing.

Without further ado, here’s WaveNet’s original piano compositions.

Track 1:

Track 2:

Track 3:

Track 4:

Track 5:

Track 6:

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