Machine Learning audio classification with Thundercomm Rubik Pi and Edge Impulse
. Plug in the power supply, USB mic, and Ethernet cable to the Rubik Pi. . Connect the other end of the Ethernet cable to your router. . Press the power button and wait for the Rubik Pi to boot. Note: this tutorial assumes a Rubik Pi with Canonical Ubuntu for Qualcomm platforms . Log in to your router’s admin interface to see the IP assigned to the Rubik Pi. . SSH into the Rubik Pi user: ubuntu pass: ubuntu
Run the following commands to install Edge Impulse on the Rubik PI
$ sudo apt update $ wget https://cdn.edgeimpulse.com/firmware/linux/setup-edge-impulse-qc-linux.sh $ sudo apt install selinux-utils $ source ~/.profile $ chmod +x setup-edge-impulse-qc-linux.sh $ ./setup-edge-impulse-qc-linux.sh
Connect the USB Mic and run the following to increase the volume $ alsamixer
Press F6 to select the USB mic input and press several times arrow up to increase the volume.
Exit and run the following commands to allow Python and Gpio:
$ sudo apt install python3-pip $ sudo apt install python3-periphery $ sudo groupadd -f gpio $ sudo usermod -aG gpio ubuntu $ sudo nano /etc/udev/rules.d/99-gpio.rules
Add these lines to the file
SUBSYSTEM=="gpio", KERNEL=="gpiochip*", GROUP="gpio", MODE="0660"
SUBSYSTEM=="gpio", ACTION=="add", PROGRAM="/bin/sh -c 'chown root:gpio /sys/class/gpio/export /sys/class/gpio/unexport; chmod 220 /sys/class/gpio/export /sys/class/gpio/unexport'"
SUBSYSTEM=="gpio", ACTION=="add", PROGRAM="/bin/sh -c 'chown root:gpio /sys%p/direction /sys%p/value; chmod 660 /sys%p/direction /sys%p/value'"
Ctrl-X and Yes to save and exit.
$ sudo udevadm control --reload-rules $ sudo udevadm trigger $ sudo reboot
Run $ edge-impulse-linux-runner –clean
Select the project, unoptimized, select the USB mic
You should see an output like:
classifyRes 2ms. { street: 0.9999, glass: 0.0001 } classifyRes 2ms. { street: 0.8629, glass: 0.1371 }
Then you can run glass.py