Comet


Not Christmas Songs

Posted by: Ryan 1 year, 9 months ago
Categories: Music

I have been developing this Spotify playlist full of songs that sound like Christmas songs, but are actually not Christmas songs.  This is a great playlist for someone who's natural tendency is to be a grinch, yet still wants to get into that festive mood.

I was adding songs to this for a few months, and then just recently scoured the internet to find some more tracks to help fill it out.

Serving suggestion: Listen while drinking a heavily sweetened white russian (which may look/taste a bit like eggnog) and some peppermint hard candies (which look like candy canes...wait what do candy canes taste like??)

Click here to access the playlist (will require a spotify account)  - I set it as a "collaborative playlist" - I think that means that anyone can add more songs.  So...I guess we will see how that pans out.

Disclaimer: I am not going to claim that all of these songs are good.  All I am saying is that they sound sorta like Christmas songs.

For those we don't have Spotify...sorry.  I pasted the tracklist below (for what it's worth).

 

Track Artist Album
Hospital Food David Gray Life in Slow Motion
White Winter Hymnal Fleet Foxes Fleet Foxes
Two Weeks Grizzly Bear Veckatimest
Standing in the Back at Your Show Wild Ones Mirror Touch
Days The Drums Portamento
Amour Amour Livia Blanc Amour Amour
Step Vampire Weekend Modern Vampires of the City
Phantom Limb The Shins Wincing The Night Away
Welcome Home, Son Radical Face Ghost
Somewhere Only We Know Keane Hopes And Fears
Soul Meets Body Death Cab for Cutie Plans
Heartbeats José González Imperial Recordings Best Of
I'll Be Home Harry Nilsson The Best Of Harry Nilsson
Disarm The Smashing Pumpkins Rotten Apples, The Smashing Pumpkins Greatest Hits
Beth/Rest Bon Iver Bon Iver
Zeal Tchami Revelations EP
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Machine Learning Progress Update 3

Posted by: Ryan 1 year, 9 months ago
Categories: Comet
Tags: Machine Learning

It’s time for another Machine Learning update.  This one is only tangentially related to Machine Learning.  In this post, I am going to spill the beans on how I made 46 cents in only 12 hours – and I only had to pay an up-front cost of about $280 to make it possible!

.........

About a month ago, I bought a new graphics card for $280 to use for neural network training.  It’s a weird thing, but in addition to rendering computer graphics, graphics cards are also very good at performing large computations that require a lot of “parallel processing”.

There is another thing that requires a lot of parallel processing – and that’s mining cryptocurrency. What is a cryptocurrency? – and what does it mean time “mine” them? - both legitimate questions that I honestly didn’t have great answers to about a week ago.  But I spent some time learning, and I just mined my first ever 46 cents worth of cryptocurrency over the last 12 hours.

Cryptocurrency

You’ve probably heard of Bitcoin, which is the most famous cryptocurrency.  Or perhaps its infamous, because it got a lot of news coverage for being an “anonymous” currency that could be used to buy illegal things.  I initially wrote a lot about this controversial aspect of bitcoin, but decided to pull it from the post because it's just not that interesting.

In a general sense, cryptocurrency is a decentralized electronic currency.  The “decentralized” part means that no singular entity sets its value or manages a ledger.  All transactions are traced by all users – so when Lisa pays Tony a bitcoin, literally every bitcoin user would be able to see it (if they wanted to).  But they wouldn’t necessarily know that it was Lisa and Tony – they would just see that account A4E23B11 paid 1 bitcoin to account 676BC144.

The beating heart of all cryptocurrencies is a technology known as the “blockchain”.  This was a breakthrough invention made by an unknown person (or group of people) under the pseudonym Satoshi Nakamoto.  I will have an entire post to write about Nakamoto – maybe later this week.  The blockchain is a giant database that is concurrently maintained by millions of users; it contains information such as how much money every user has, as well as a log of every transaction.  Without going into too much technical detail, the blockchain is able to do accomplish this thanks to the work of “miners”.

Mining Cryptocurrency

Miners are people who dedicate their souped-up computers to the task of processing transactions and maintaining the ledger for the cryptocurrency.  The ledger is essentially compacted using a very difficult-to-solve math problem that all miners’ computers labor at trying to solve.  Solving it comes down to luck, but a faster computer can attempt to solve it more times per second than a slower computer.  The lucky computer that succeeds at solving the problem first gets rewarded 12.5 bitcoins – and since 1 bitcoin is equal to roughly $11,500, that comes out to about $140,000.  Not bad!  One of these is solved about once every ten minutes.  Take that, Powerball…

So, obviously, the chance of successfully solving this problem before anyone else is very small, especially with a cheap $280 graphics card.  There are miners out there who have rooms full of purpose built mining rigs that sound like a jet engine taking off – their electric bills from mining alone can be upwards of $1,000 per month.

So I clearly didn't make $140,000 - but, you might be wondering how I made 46 cents.  I did this by joining a large pool of miners.  By joining the pool, I agree that if my computer finds the solution, I will split the profits with everyone else based on how fast everyone’s computers are in the pool.  And if someone else finds the solution, they need to split it with me too.  Well, some folks in my pool must have hit it, and me and my baby graphics card got our 46 cents worth!

Now, 46 cents in 12 hours shouldn't be coughed at.  If I do some tweaking I could possibly get that up to $1.50 per day.  That comes out to $550 per year – enough to cover the cost of the card, and then some.  And that’s not taking into account the value growth that cryptocurrency is going through.  1 bitcoin used cost only $3 back in 2012.  So there is growth potential.

Ethereum

I know that the geek is strong enough in this post as it is – but I need to take this one step deeper before I sign off.  I’ve been using bitcoin as an example in this post because most people are familiar with the term “bitcoin” – but that’s far from the only cryptocurrency in existence.

The second most popular cryptocurrency is called “Ethereum” – and it takes the blockchain idea established by bitcoin and pushes it to the next level.  The defining characteristic of Ethereum is that it allows entire programs and all sorts of other data to be stored within the blockchain – not just a ledger.  I am having a hard time wrapping my head around the possibilities of Ethereum, but my gut reaction is that it could become big in the next few years.  So, I chose to spend my time (and I earned by 46 cents) mining Ethereum rather than bitcoin.

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abco

Posted by: Ryan 1 year, 9 months ago
Categories: Music

Last night, Jess and I went to our good friend Abby's graduation from her DJ program.  She was the very last act to perform, she played a great set, and the turnout was definatley solid.

There was a driving force that inspired Abby to take this DJ class, despite all of the various things that make life "busy" - and I admire that.  There is something to be said about persuing hobbies and making dreams a reality.

I think this struck a chord with me because I have been finding very little time to persue my interests because of work commitments lately, and that can get frustrating.  I am trying to use this blog to keep the ball rolling on the machine learning stuff.  Anyways - I wasn't planning on making this about myself when I started writing.  Nice job on that set, Abby.  Her website is: https://www.abcomusic.com/

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Tiny Desk

Posted by: Ryan 1 year, 9 months ago
Categories: Music

I'm a big fan of NPR Music's Tiny Desk concert series. These concerts started in 2008 - almost 10 years ago - supposedly when Bob Boilen (host of the All Songs Considered podcast) became frustrated with noisy and distracting concert venues. The Tiny Desk concerts are recorded right at Bob's office desk - and they are great because they strip away all of the cruft that gets between the music and your ears.

The music is front and center, and artists seem to have great respect for that.  They treat their Tiny Desk performances as something special.

I feel like the quality of music has been especially high over the past few months, so I decided to share a list of my favorite Tiny Desk concerts.  I haven’t come anywhere close to watching all of the concerts, so I might have to update this list again sometime in the future.

The Roots (featuring Bilal)

Gotta love this.  The vocalist's (Bilal's) performance is powerful yet it often seems effortless.  This sort of thing only happens when there is passion behind the music - and I sense there's a lot here - particularly in the last 3 minutes or so when he seems legitimately pissed off about equality issues.  Definately one of the best Tiny Desk concerts right here.

Albin Lee Meldau

This guy is a bit of a contradiction. He has a really soulful voice that sounds like it comes from New Orleans - yet he's from Sweden.  Nonetheless, he's got the goods!

Benjamin Booker

He sounds a bit like Ray LaMontagne.  Solid concert here – it’s so easy to watch that it will be over before you even know it – and then you’ll say, ‘is there more?’  Yes there is - and it was recorded at Newport Folk Festival.

Joseph

This is three sisters in a folk band.  The vocals are the main focus here.  Lots of good harmonies, and just the right amount of yelling.

Red Baraat

With Tiny Desk, I find that a band completely outside of my typical preferences can grab my attention - and impress me!  This is a good example.  Honestly – I dare you to play the first 10 seconds of this, and then not listen to at least 5 minutes.  It’s got a guy playing an electric guitar with a violin bow.  It’s got an Obama lookalike playing drums.  I’m pretty sure the lead singer starts handing out snacks near the end.  Everyone is having so much fun here.  I’d join their band if they asked me.  Mom do you still have my clarinet from elementary school?

The Arcs

I didn’t know who The Arcs were before I watched this concert.  This is a great band, perhaps underappreciated.  Also, this was recorded two years ago and it was the 500th Tiny Desk concert.  So, estimating that there has been about 150 additional concerts since then, and that each concert is about 15 minutes long, that means there is about 9,500 minutes or 158 hours of music – and more getting added every week.  Now you know why I haven’t listened to all of the concerts.

Oddisee

This is a hip-hop artist, and his Tiny Desk performance is great.  Writing about music is hard - but the person who writes the YouTube descriptions for NPR music is on point - they said this: "His deceptively intricate rhythm tracks interlock with complementary harmonies and brilliantly constructed bars in ways that appeal to both diehard hip-hop heads — those who decipher and analyze lyrics as a hobby — and those who just want a clutch beat."  Woah, now those are the words of someone who actually knows what their doing.

Lucius

When these guys finish their set, the host - Bob - was so excited that he gave the band high fives, and then catches them off guard by asking for another song.  They didn't seem prepared, but they grabbed random office supplies to use as instruments - and then proceded to jam.

St. Paul and the Broken Bones

Standing on the desk of Tiny Desk is a move you see from time to time – but Paul Janeway, the lead singer, pulls it off better than most.  Soulful.

(Honorable Mention) OK Go

In traditional OK Go fashion, their Tiny Desk concert is an intricate video recorded during NPR Music’s move from one office to another.  They recorded little segments of the song in the first office, then in the hallway, then in the moving truck, then in the new office elevator, all the way to the new Tiny Desk location.  This isn’t really a “stripped down” intimate performance that is typical of Tiny Desk – so I would feel disingenuous putting it in the list with the others.

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Text formatting

Posted by: Ryan 1 year, 9 months ago
Categories: Metaposts

The "Contribute" page should now allow you to format your text much more easily!  I thought this would be an simple thing to implement, but - nothing ever is!!

Picture is unrelated, it is Jess hanging out with Matt Lauer during a visit to NYC a few years ago.  Jess told me she got bad vibes from him.

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Some videos by a movie accent expert

Posted by: Ryan 1 year, 9 months ago
Categories: Web
These are some good videos made by a Hollywood voice/dialect coach. He is able to pin-point extremely specific things that are correct/incorrect about actors' portrayals of accents and impressions.

video on accents

video on actors portraying other people
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Machine Learning Progress Update 2

Posted by: Ryan 1 year, 9 months ago
Categories: Comet
Tags: Machine Learning
It's time for another machine learning update...

I programmed the artificial neural network from my previous post to process a video feed from my camera. Then, I pointed the camera at a youtube video of traffic to see if it could detect cars.

And, I am happy to say yes, technically it can detect cars from the video. It's a little hit or miss right now, but it can definitely pick up about 50% of the cars that pass by. Not only that - but the camera isn't hooked up to my desktop computer. I have this whole program (minus the youtube video) running on a $35 mini computer that is about the size of a deck of cards (Raspberry Pi). It's processing video at just under 3 frames per second.

I will need to work on getting that frame rate up higher - I'd like for it to be at least 5 frames per second, and 10 would be ideal. More importantly, I need to improve its comprehension. The photos that I trained the neural network on were mostly of cars taken from the side or taken from the back. If the training photos included some taken from above and at diagonal angles, I think the performance would improve a lot.

Below is a gif of the neural network in action. The red boxes are where it's looking. The boxes turn green when it thinks it sees a car. Yeah, obviously it has a ways to go - but it's a start.

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Twiddle

Posted by: Ryan 1 year, 10 months ago
Categories: Music
Last Saturday, we went to see a band named Twiddle in Boston. The music sounded great. They are a jam band from Vermont. They are in the same genre as Phish and DMB, but they seem to stick to an "up-beat" funk sound. Clearly a lot of talent in this band.

We saw them in a venue called The Paradise - as you can tell from the photo, it's a pretty small place. I thought the venue was fun. I think some bands that are popular enough to fill a larger venue (such as the Orpheum, House of Blues, or Blue Hills Pavilion) would really be more at home in a place like this. Sometimes the music just belongs in a smaller venue without seats. I'd be willing to pay more for the tickets, really.
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Machine Learning Progress Update 1

Posted by: Ryan 1 year, 10 months ago
Categories: Comet
Tags: Machine Learning
Picking up from where I left off on my previous machine learning post, which you can find here...

I am embarking on a personal project involving machine learning and computer vision. The project has four main goals:
  1. Develop an artificial neural network that can identify vehicles within an image.
  2. Optimize that neural network so it can scan between five and ten images every second (in other words, allow it to operate on a video stream).
  3. Further optimize that neural network so it can run on a $35 mini-computer known as a Raspberry Pi (rather than my large desktop computer).
  4. Develop some computer routines that utilize the above and collect data.
  5. Learn as much as possible in the process.

Tonight, the project reached its first milestone: I programmed and trained my first artificial neural network. I trained it by showing it 6,592 images - half of which contained a car, and half did not contain a car. After viewing each image, the neural network would say whether or not it thought the image contained a car. If it guessed wrong, it would re-calibrate itself, and then we'd move to the next image.

The above sounds like a really boring a tedious process - but it's not. The training is all automated, and it completed in under one hour. After training, I showed the neural network about 750 images that it never seen before, and it was able to predict whether or not they contained a car with over 95% accuracy.

These accomplishments are rather mundane in the world of computer science - but its a humble start to the project. I can't make the neural network too flashy, or else Steps 2 and 3 will get very difficult.
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