Applications in Music

One program that does not quite require this is, a program called Variations, developed by Bruce L. Jacob. This program uses genetic algorithms that are evolved the the operators liking, then it composes and listen to the piece to decide whether or not it is good. B.Jacob describes how this genetic "ear" works in his Composing With Genetic Algorithms paper:

"...The [ear] module is a collection of chromosomes, each of which acts as a data filter that identifies harmonic combinations as “good” or “bad.” Before composition begins, the chromosomes are evolved to reflect the musical tastes of the human operator. First, a set of randomly-generated ear chromosomes are auditioned on how well they filter material. The evaluation mechanism in this process, as in virtually all other genetic music studies, is a human judge. Musical examples are created and passed through the ear chromosomes, and the human operator assigns weights to chromosomes according to how well they agree with his or her inclinations. Chromosomes with high marks are more likely to reproduce and have their alleles present in the next generation. Successive generations therefore exhibit the best traits of previous generations. Once there is a satisfactory set of filters, the [music creating] process...begins..." [B.Jacob]

- Demonstration of variations. (RA: 124Kb, WAV: 526Kb)

A program such as variations creates music autonomously, but what about dynamically? The best example of dynamic music creation is GenJam - or Genetic Jammer. This program by Al Biles listens to him play through a device called a pitch-tracker that converts the notes from the trumpet into MIDI notes. The program then will improvise using an original set of notes. Biles will then tell GenJam whether the sequence it just played was good or bad. On the next time round, then program will get rid of the tunes that sounded bad, and evolve new parts from the good sounding songs. GenJam uses a genetic algorithm-based engine to develop its notes and riffs.

Transcription is in many ways even harder than composition due to the nature of the human ear and brain. A "trained" human can distinguish instruments, pick out melodies from bass lines, hear the difference between the drums and a guitar. With a computer, since everything is represented in 0s and 1s, it makes life a little harder! For a computer to listen to a 4-member rock band playing, and to create 4 written pieces of music to correspond perfectly with the guitar, bass, drums and singer would be an incredible task. To show just how hard this task would be, we'll look at one instrument, and how hard it would be to transpose that instrument alone!

The Guitar

Waveforms
First of all, lets look/hear a guitar playing a single note:
- Simple A-note. (RA: 9Kb, WAV: 165Kb)

Notice how the waveform repeats perfectly - this indicates that only one note is being played? Despite this perfectly repeating waveform there is still a small problem. An A-note is mathematically 440Hz, which is a perfect sine wave shape (as shown). So, why does an A-note from a guitar look different? Because of a phenomenon called overtones. When you play an A-note on a guitar, not only do you get 440Hz, you get 220, 880, and others at different octaves. 440Hz is the dominant waveform, but the other tones are noticeable when you look at the waveform. This is small problem, though, and by merely finding the dominant tone, you have your note! Electronic tuners, MIDI guitars, and harmonizers are a perfect examples. Yet, the minute things get a bit more complicated, so does the waveform, and consequently the computer has a much harder time figuring out the sequence of notes.

- Arpeggio, demonstrating more complicated music. (RA: 14Kb, WAV: 296Kb)

Effects and Speed
Now, this are already complicated - but we have two more things to complicate guitar transposing - effects, and speed. Electric guitars are barely ever played clean, they nearly always have some effect on them (mostly delay and distortion). Speed is another factor, guitars can be played at incredible speeds, quicker than most other instruments. These next two sound clips merely demonstrate this. This first one demonstrating the effects (distortion), the second one speed and effects (distortion, wah pedal):

- Blues scale, demonstrating complication due to effects. (RA: 11Kb, WAV: 229Kb)

- Tapping, demonstrating complication due to speed and (more) effects! (RA: 9Kb, WAV: 161Kb)

Positioning
Lets say that notes now can be perfectly turning into a MIDI file, or another similar format. Now, how about using AI to create tabulature? Easier said than done, since the same phrases on a guitar can be played many different ways. The higher up the fret board, the further you can stretch, and the more you can take advantage of the "same note/different string" technique on the guitar. Of course, you don't want to be forever jumping around the guitar bar-to-bar to get the best position for just that bar. You need to be able to play the entire place fluidly with huge fretboard jumps only when necessary. Thus, a program that converted MIDI to tab would require to look at both the bar before and after the current bar to decide the position to take. Perhaps taking parameters such as the players fingering skill, hand-stretch and picking skills could be taken into account to lessen the load on the computer (making a more generic one would require a bit more effort) and create more user-specific tabs.