recorded 8 notes (a scale starting on c = 262 Hz), three times each, on the trumpet, plus three times each note both of the toy instruments (piano toy “ptoy” and alligator toy “atoy”). uploaded the recorded sounds to euclid. now i am running a ff network on the recorded trumpet samples.

here are results from the same network as the previous post on a recording of two notes being played at the same time.

i recorded two more samples of each note on the glockenspiel-like instrument, then changed the network so it randomly selects one of the three for each note when generating training patterns. after training on 20000 patterns here are the results for the same scale as before:

results are much better than before. there don’t seem to be any major “gray areas”. the only problem is that every single note seems to pick up something when just one note is played.

i started testing how the network does with recordings that i did of real instruments today. i’m using goldwave to record with a apple pc microphone hooked up to an “iMic” usb sound interface. i did recordings for a glockenspiel-like instrument with four notes. it’s lower-pitched than the old samples i was using, so i brought the inputs back down from 72 to 64. there is some noise in the recording’s background, and i’m using goldwave’s noise reduction function to get rid of most of it. i trained the network for 20000 training patterns, and here is the output of a scale i constructed out of the training pattern notes:

here is the same scale, but re-recorded when played on the instrument:

it seems to hit the notes, but it also hits a bunch of other stuff that wasn’t being played. (i think the long duration of the second note might actually be correct, since that note was played quite a bit harder than the others.) a way to solve this problem might be recording multiple training patterns for each note, then the network might be able to generalize better.

here are the intensities over time for the four-note glockenspiel samples i’ve been using. the first one is much lower than the others, which could explain why it was not showing up on the linear output node tests.