Things Not to Do In My Garden Next Year
Things learned as another season of stump farming draws to a close:
- I will try to avoid squeezing fifty-thousand tomato plants into three inches of dirt – it makes it frickin impossible, and uncomfortable, to extract any fruit from within the clutches of all those stems. They need about a two foot radius each.
- No more cantaloupe. Cantaloupes DO NOT work well in this climate. The space required/taste acquired ratio is far too high. Although… it is kind of awesome that they actually grew. Now I can be proud of attaining the life accomplishment: Has Grown Melons.
- Don’t be stingy with the beans. Next year I will use all the cantaloupe space to erect the most enormous bean teepee the gardening world has ever laid it’s green-fingered eyes on. Beans are promiscuous little holy grails. They are easy, delicious, and you can make a bloody teepee out of them. One cannot argue with a teepee.
- Don’t bother planting tonnes of chilis. This year I stuck a Jalapeno and Habanero plant on my pseudo-balcony and they have been way more than adequate. Acres of chilis in the real garden are totally unnecessary, they just end up unused and rotting.
- Don’t put off weeding and turning soil over until the spring. Spring Ben hated previous-Autumn Ben this year for being a lazy so-and-so and letting everything overgrow.
- Do grow… no wait, this list is supposed to be all negatives. Don’t, errr, not grow dyers indigo.
Music Information Retrieval in a Post Bedtime Environment
Oh dear, instead of being asleep I/we/me are perusing this guys thesis (PDF warning). His PhD project centered on writing software that can automatically identify the identity of a singer by analyzing a recording of their performance. The really neat thing is that it works for recordings which have other instruments in them, so he had to come up with a way to determine whether certain sounds we’re voice-like or instrument-like.
This thesis reading came about due to me taking another poke at my previous attempts at music visualization (or more fancy-schmancily: MIR – Music Information Retrieval). Like so many research related items, although the general concept is fairly well-defined and straightforward the devil is definitely in the heapings of details.
There are all kinds of issues that rapidly start cropping up after the basics have been sort of sorted out.
For example, humans are pretty good at picking a melody. It’s hard to tell a computer to do that. As soon as you have more than one note being played at a time it’s a very non-trivial problem to work out which notes belong to the melody and which to the accompaniment. Particularly if you have, for example, a melody which switches from high to low notes, or passes between instruments, or has sharp changes in its dynamics.
In short, this means it is pretty easy to extract the melody from a monophonic performance of “Mary Had a Little Lamb”, but nearly impossible to extract it from anything else. At least automatically.
However, this brings up an interesting point. Can people even accurately identify which notes form the melody in a piece of music? Here’s a quote from the above thesis which alludes to what I’m getting at, which in this case is talking about automatically identifying musical instruments:
Martin [41] examines the classification of isolated samples from 37 instruments using
hand-picked features as inputs to a quadratic classifier … best-case accuracy is reported at 71.6%.
Martin [41] and Brown [51] also cite human performance for instrument identification tasks. Brown notes that human performance on isolated tones for her two-instrument identification task is comparable to her system’s performance. Martin finds that humans score much worse than his system (i.e., 50-67% versus 71.6%) on the instrument identification task and with comparable accuracy for instrument family identification.
Some MIR goals might not actually be possible. This is possibly true for 100% accurate two-instrument identification, but I bet it’s definitely true for melody extraction. In fact, the more I consider this problem the more I realize that it would be frickin’ impossible to say which of the notes in a piece from part of the melody.
It’s like when you try whistling a famous piece. Whistling should be a perfect example of melody recall, right? We whistle the melodies we have extracted from music, it’s our best attempt at melody identification. Well, anyway, when I try to do this the first part usually goes pretty well, you can sometimes make it all the way through the exposition without getting too confused. However, as soon as you hit the development section you’re basically screwed. You try to whistle three parts at once and it falls apart like a… a… buttercup.
At least, that’s what happens when I try. Now I’ll probably get smarmy eMails from music majors who can whistle every orchestral part of Beethoven’s Ninth all the way through. At once.
Free Sound Samples
This is as much for me to remember as for you to use — I’ve taken to using some of these blog entries as a sort of bookmarking system — but we’re dead dead certain that some of you will have a use for freesound. It’s a library of creative commons licensed sounds, so you can use them in whatever project you feel is necessary to present to the world. Like an animated cartoon (yeah, as opposed to an unanimated cartoon. What are you going to do about it?) about your adventures reading my blog.
My fellow American’s, we must solemnly pledge to add sound effects to everything we create from this day forward.
The Benefit of No Opinions
One of the problems people face when getting into classical music is that it is sometimes hard to form independent opinions about a piece, particularly if they are negative. This is of course because of the untold piles of pre-established opinions which have been heaped and draped and taped across every major composition by generations of critics. Not so fond of Beethoven’s ninth? Well then you are an uncultured fool, say the decades of beardie and serious-looking music-reviewers, musicologists, musicians.
It is hard to form an independent opinion.
For example, one of the first composers I got into was Mendelssohn. Unfortunately, not soon after discovering him, up popped a selection of liner notes describing his music as not very serious, and him as essentially a second-rate composer. This kind of description is really disheartening to read after you are just getting your teeth into the music. You have erred. You do not have the correct taste. You should like Beethoven and Bach and Brahms a lot more than you do.
This insidious background of previous opinions has been particularly hammered home to me recently after listening to — and liking a lot — a composer who has had little to nothing written about him. I would like to say that this lack of opinionizing has not affected me not once, not a bit, not slightly: but that would be disingenuous. I guess I still feel like a bit of an outsider to the sphere of classical music, and not having that hint of external validation nags away in the background.
It’s kind of silly really. Music appreciation should be a subjective thing (shouldn’t it?) and it really shouldn’t matter whether other people appreciate your chosen choice of piece. I think that it is somewhat telling as to the attitude and atmosphere of the classical music genre that I still feel this way after several years of listening. Sure, all musical generes are like this to somewhat extent (“What do you MEAN, you don’t like the Beatles?!”) but the classical music world seems degrees snobbier.