Engineers describe a system whose input and output behavior are known and whose inner workings are otherwise mysterious as a black box. Bruno Latour describes the black box as:
the way scientific and technical work is made invisible by its own success. When a machine runs efficiently, when a matter of fact is settled, one need focus only on its inputs and outputs and not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become. Continue reading “Inside the black box”
The parts of the computer that do the “thinking” are mostly made of little electronic switches called transistors. If you connect two wires to a transistor, you can use the voltage on one wire to control the voltage on the other. What’s especially handy for engineering purposes is that the presence or absence of a small voltage on one wire can control a wide range of voltages on the other wire. When voltage on the control wire changes, the transistor opens or closes the other wire to the flow of electricity in much the same way that a faucet controls the flow of water in a pipe.
Technology keeps getting better. Do our lives get better as a result? In certain specific ways, maybe yes, but in general, I would say, not really. How is that possible? I think there are two big things at work. Technology is evolving semi-independently of the humans that produce it. We don’t control the evolution of our tools any more than we control the evolution of our gut fauna or infectious diseases. Also, the pace of technological change is a lot faster than the pace of our genetic evolution. Our brain anatomy is having a hard time keeping pace with the changes in the world that we’re making inadvertently with our tools.
Sampling, remixing and mashups make some people angry. A lot of people think that repurposing existing ideas is bad, that it’s lazy, or a form of stealing. We value originality highly. Should we? My own experience of music making is that there are no original ideas. There are novel combinations of old ideas, but it’s neither possible nor desirable to make a genuinely new and unprecedented piece of music. If you want to hear truly original music, bang randomly on a piano keyboard. You’ll be playing something new and unprecedented, but it probably won’t be something you’d want to hear twice.
In my laptop band Revival Revival, we use Reason for all of our instrumental sounds and sample playback. The newest version has a handy color-coding feature in the sequencer, which makes it easy for me to be able to keep track of which part of which song happens in which order. Having all the tunes under my eyes all the time has revealed new wisdom to my ears about symmetry and asymmetry, and isn’t that what music is all about?
The color-coding system started as a simple information-management technique, but it ended up improving my ears. Spending so much time looking at these colorfully abstracted representations of so many songs, I couldn’t help but notice some patterns. I’ve done enough tracks now that I can lay something out in the sequencer and know that it’ll basically work without having to listen to it first. Classical and jazz musicians get to the point where by glancing over a score, they can hear it quite clearly in the mind’s ear. The Reason sequencer has a much shorter path into the brain’s deep sense-data processing centers because it’s dynamic, animated, and responsive to my thoughts in real time.
When you delve behind the scenes with the internet, you immediately come face-to-face with a lot of threatening computer gibberish. The most menacing codes are the ones that stand for colors, random-seeming strings of letters and numbers like #99CC66 or #4F102A. Sometimes you see colors described verbally: “black”, “white”, “blue”, etc. That’s fine for simple colors, but no good if you want exact hues. The web system for describing colors is daunting at first, but once you find out what the codes mean, they reveal themselves to be elegantly compact. If you’re willing to follow me through a little math and physics, you might find some geeky fun here. Continue reading “Robot counting for humans”
Ethanhein.com has had 465,689 hits so far, mostly distributed among my blogs and mp3 pages. A single blog post about Family Guy generates a disproportionately large percentage of that traffic. October 2008 represents a typical month, with 40,611 hits total. That’s around 1,310 a day, 54 an hour. A large percentage of those are from robots, but still, neato. These numbers don’t include hits on my Flickr photos. My various sets have had 228,155 views so far, with anywhere between 500 and 1,500 hits a day.
I’m a jazz guy. I like improvising in front of an audience. I like publishing a post while it’s still only a third finished. It keeps the fire lit under me to get the rest written. I was looking for a blog platform congenial to this method of working. Then I read a PC Magazine article, Succeed At Blogging The Gawker Way. Like a Gawker article, it’s funny, frank and packs maximum useful information into a minimum number of words:
“Get specific. Pick something that interests you. Revel in weird topics. Don’t be afraid to get conceptual. Keep it friendly (and human).”
The article gives Gawker writer Nick Douglas’ reasons for using WordPress as their platform. He’s right, WP is the bomb.
How do you get sound in and out of a computer? There are two steps. You have to turn the sound into electricity, and then you have to turn the electricity into numbers.
Turning sound into electricity
At the physical level, a sound is a rhythmic vibration of air molecules. Your ears can detect subtle changes in the air pressure, and can reconstruct good guesses about what might be agitating the air to produce those changes. When the air pressure fluctuates in a steady sine-wave pattern, you hear a musical pitch. The faster the fluctuation, the higher the pitch. This video by Vi Hart explains:
Microphones work a lot like your ears. They contain pieces of metal that vibrate in response to the vibrations of the air, generating a fluctuating electromagnetic disturbance.
Analog recording media store the fluctuating electric current as fluctuations in something else. In a vinyl record, the fluctuations are encoded in the squiggly physical shape of the sides of the groove. Magnetic tape stores the fluctuations in the alignment of tiny magnetic particles embedded in the plastic.
Turning electricity into numbers
The computer takes in fluctuating electric current and turns it into numbers. Long, long, lists of numbers. It does this with a specialized device called an analog-to-digital converter.
The analog-to-digital converter in the computer’s sound card has a clock, like the one synchronizing the activities of the computer generally. At each clock pulse, the converter takes a reading of the current on the input wire and finds the closest numerical value out of a finite set of choices. The more choices the converter has, the more accurately it can represent the input signal. A two-bit converter would only be able to tell if the input wire was on or off. A four-bit converter could sixteen different possible readings. An eight-bit converter could store 256 different readings. The more bits you have, the more accurately your digital readings can represent the original input signal. The standard for CD-quality audio is 16 bits, which means that each reading can take one of 65,536 different values. Higher bit depths are also possible — very fancy equipment can record 24-bit, 48-bit or even 96-bit audio.
Bit depth is only one factor in determining the quality of your digital audio. The other is sampling rate, the frequency with which the converter takes its readings. If the converter takes its readings more often, it gets more accurate results. Standard CD-quality audio is recorded at 44,100 samples per second. This sounds like an incredible speed, but CPU clocks routinely operate thousands of times faster than that.
The image below shows a four-bit analog-to-digital converter in action.
The red line shows the amplitude of the input wire’s voltage over time. The sixteen horizontal grey lines are the different voltage levels the converter can detect. It takes four bits of data to specify the sixteen different possible values. The tick marks on the horizontal axis are clock pulses. Each reading gets stored as a binary number. The list of numbers makes up the digital audio file.
To play digital sounds, the computer sends its list of numbers to a digital-to-analog converter. To produce sound on speakers or headphones, the converter sends pulses at the appropriate voltages out on the wires, which your ear averages out into a pretty good reconstruction of the original sine wave.
Digital audio editing
Once you have your current stored as numbers, you can do a lot of cool stuff. Any sound in any digital medium is basically a spreadsheet with two extremely long columns, one for each stereo channel. In 16-bit audio, the numbers in the columns range from zero to 65,535 (2^16 – 1.) One second of stereo CD-quality audio is two lists of 44,100 numbers each. If the values of the numbers range smoothly along a sine wave that cycles four hundred forty times per second, you hear a computery beep playing concert A. If the numbers fluctuate along the pattern you get from superimposing the sine wave with another one that cycles six hundred sixty times per second, you hear two computery beeps a perfect fifth apart. Add in another sine wave doing eight hundred eighty cycles per second and you get the I-V-I power chord beloved by rock and roll.
All of the audio editing and processing that happens in Pro Tools and programs like it boils down to systematic mathematical operations on your lists of numbers. Auto-Tune looks for sine wave patterns and alters them so they snap to the closest piano-key frequency. At the transistor level, Auto-Tune is no different from Microsoft Excel, except that it acts a lot faster on bigger lists of numbers. Copying and pasting repeated sounds is the same procedure for the computer as copying and pasting a list of numbers or a string of text.
You need a fast computer with a capacious storage capacity to do serious audio work, but we’re lucky enough to live in an era when even a garden-variety laptop can handle stupendously complex audio tasks.
The great miracle of music for me is not any particular technique or piece or performer, but just the fact that it exists at all. A single linear wave can encode all the rich complexity of all the sounds we hear. This wave is as easily translated into numbers as dollars can be translated into pizzas. The complete works of Bach or Coltrane can be encoded as simple two-dimensional waveforms. All that music is two-dimensional curves, voltage vs time, or air pressure or guitar body flexion vs time.
Our brains are stupendously adept at detecting patterns of patterns of patterns in the linear waveform of air pressure, deconstructing and comparing the component sounds that went into it. If there are multiple frequencies present simultaneously in the pattern of vibrations, we can distinguish them and, with a little training, detect the ratios between them. I feel like we’ve barely begun to scratch the surface of the artistic possibilities of mathematical operations on numerical audio data.
