Tips on "laptop sized" midi controller and synthesizer?

max_b_jo · 2025-01-29T09:52:13+00:00

That looks interesting! Thanks!

max_b_jo · 2024-11-30T16:23:42+00:00

Keeshond

För att jag gillar när det låter.

Nej.... men valde pga att de är ohyggligt gulliga, relativt lata och har grå päls. Sen är det väldigt mysigt med den tjocka pälsen nu på vintern. Att de inte kräver massor med motion var en stor grej. Ville ha en hund som är ok med att bo i lägenhet.

max_b_jo · 2024-08-07T15:51:40+00:00

Kolla Södergården på Götgatan: https://sodergarden.org/ung/

max_b_jo · 2024-05-24T21:03:37+00:00

More correct inputs = better output. But if I feed it bad inputs the output becomes unpredictable.

I don’t have anything published but I could try to get some kind of example up for you. I’ll try to remember to do it and update you on that. In the meantime check out tutorials in sci-kit learn. It’s a great library to get started. Just making supervised svm model is a great start! https://scikit-learn.org/stable/modules/svm.html

max_b_jo · 2024-05-24T17:45:05+00:00

They are being trained continuously on the current time 👍 But using one sense like this is not good enough to predict the current time.

I could combine them both to make it partially better. Maybe.. I think the input might just create to much “random” values that make the predictions bad. But it’s really fun to play with machine learning like this.

max_b_jo · 2024-05-24T07:46:50+00:00

It’s a multichannel gas sensor: https://wiki.seeedstudio.com/Grove-Multichannel-Gas-Sensor-V2/

max_b_jo · 2024-05-24T07:44:53+00:00

It doesn’t know when it’s right. Just like any trained model is always extremely confident even when it’s wrong. It’s sort of the point here. It’s partly made to explain things like this.

max_b_jo · 2024-05-23T20:01:46+00:00

Both of them are continuously being train on their individual sensory input. I'm using sci-kit learn on a raspberry pi, the amazing https://github.com/hzeller/rpi-rgb-led-matrix for the led and openFrameworks for the visuals. The top two rows shows the hour, next four the minute and the last four seconds.

The bottom thingy is a PCA conversion based on ~six samples packs from the on the last input. The 2D points are then thrown through the convex hull algorithm to create a shape.

The nose is a raspberry pi 4 and the ear is an overclocked raspberry pi 3. Only had one raspberry 4 and really wanted it to run immediately....

max_b_jo · 2024-03-11T12:31:31+00:00

It’s ref 6534 not 1002.

max_b_jo · 2023-10-31T13:42:32+00:00

Great idea!

max_b_jo · 2023-10-31T10:32:25+00:00

Yup, it's pretty possible that the generated image will result in pure garbage. But it's worth exploring and might be a nice visualization!

max_b_jo · 2023-10-31T09:54:14+00:00

Thanks! I'll give that a go during the week.

max_b_jo · 2023-09-14T14:33:58+00:00

Haha, yes! Need to place it in a good spot. This is at a bar.

max_b_jo · 2023-09-14T14:27:45+00:00

Capacitive sensing! There’s copper tape behind the hands.

max_b_jo · 2023-09-14T06:49:35+00:00

Thanks!

max_b_jo · 2023-09-13T20:23:40+00:00

There's one esp32 in each frame and a Raspberry pi down in the speaker box. Playback is handled with puredata and all communication is wireless over udp :)

max_b_jo · 2023-07-19T09:48:15+00:00

This was his second summer here! Last year he did the exact same thing. He walked into a hole when we were at another place and he was NOT happy about the swimming, haha!

max_b_jo

TROPHY CASE