Using a raspberry pi, we listen to the coffee machine and count the number of coffee consumption

Overview

maintained by dataroots

Fresh-Coffee-Listener

A typical datarootsian consumes high-quality fresh coffee in their office environment. The board of dataroots had a very critical decision by the end of 2021-Q2 regarding coffee consumption. From now on, the total number of coffee consumption stats have to be audited live via listening to the coffee grinder sound in Raspberry Pi, because why not?

Overall flow to collect coffee machine stats

  1. Relocate the Raspberry Pi microphone just next to the coffee machine
  2. Listen and record environment sound at every 0.7 seconds
  3. Compare the recorded environment sound with the original coffee grinder sound and measure the Euclidean distance
  4. If the distance is less than a threshold it means that the coffee machine has been started and a datarootsian is grabbing a coffee
  5. Connect to DB and send timestamp, office name, and serving type to the DB in case an event is detected ( E.g. 2021-08-04 18:03:57, Leuven, coffee )

Raspberry Pi Setup

  1. Hardware: Raspberry Pi 3b
  2. Microphone: External USB microphone (doesn't have to be a high-quality one). We also bought a microphone with an audio jack but apparently, the Raspberry Pi audio jack doesn't have an input. So, don't do the same mistake and just go for the USB one :)
  3. OS: Raspbian OS
  4. Python Version: Python 3.7.3. We used the default Python3 since we don't have any other python projects in the same Raspberry Pi. You may also create a virtual environment.

Detecting the Coffee Machine Sound

  1. In the sounds folder, there is a coffee-sound.m4a file, which is the recording of the coffee machine grinding sound for 1 sec. You need to replace this recording with your coffee machine recording. It is very important to note that record the coffee machine sound with the external microphone that you will use in Raspberry Pi to have a much better performance.
  2. When we run detect_sound.py, it first reads the coffee-sound.m4a file and extracts its MFCC features. By default, it extracts 20 MFCC features. Let's call these features original sound features
  3. The external microphone starts listening to the environment for about 0.7 seconds with a 44100 sample rate. Note that the 44100 sample rate is quite overkilling but Raspberry Pi doesn't support lower sample rates out of the box. To make it simple we prefer to use a 44100 sample rate.
  4. After each record, we also extract 20 MFCC features and compute the Euclidean Distance between the original sound features and recorded sound features.
  5. We append the Euclidean Distance to a python deque object having size 3.
  6. If the maximum distance in this deque is less than self.DIST_THRESHOLD = 85, then it means that there is a coffee machine usage attempt. Feel free to play with this threshold based on your requirements. You can simply comment out line 66 of detect_sound.py to print the deque object and try to select the best threshold. We prefer to check 3 events (i.e having deque size=3) subsequently to make it more resilient to similar sounds.
  7. Go back to step 3, if the elapsed time is < 12 hours. (Assuming that the code will run at 7 AM and ends at 7 PM since no one will be at the office after 7 PM)
  8. Exit

Scheduling the coffee listening job

We use a systemd service and timer to schedule the running of detect_sound.py. Please check coffee_machine_service.service and coffee_machine_service.timer files. This timer is enabled in the makefile. It means that even if you reboot your machine, the app will still work.

coffee_machine_service.service

In this file, you need to set the correct USER and WorkingDirectory. In our case, our settings are;

User=pi
WorkingDirectory= /home/pi/coffee-machine-monitoring

To make the app robust, we set Restart=on-failure. So, the service will restart if something goes wrong in the app. (E.g power outage, someone plugs out the microphone and plug in again, etc.). This service will trigger make run the command that we will cover in the following sections.

coffee_machine_service.timer

The purpose of this file is to schedule the starting time of the app. As you see in;

OnCalendar=Mon..Fri 07:00

It means that the app will work every weekday at 7 AM. Each run will take 7 hours. So, the app will complete listening at 7 PM.

Setup a PostgreSQL Database

You can set up a PostgreSQL database at any remote platform like an on-prem server, cloud, etc. It is not advised to install it to Raspberry Pi.

  1. Install and setup a PostgreSQL server by following the official documentation

  2. Create a database by typing the following command to the PostgreSQL console and replace DB_NAME with your database name;

    createdb DB_NAME
    

    If you got an error, check here

  3. Create a table by running the following query in your PostgreSQL console by replacing DB_NAME and TABLE_NAME with your own preference;

    CREATE TABLE DB_NAME.TABLE_NAME (
        "timestamp" timestamp(0) NOT NULL,
        office varchar NOT NULL,
        serving_type varchar NOT NULL
    );
    
  4. Create a user, password and give read/write access by replacing DB_USER, DB_PASSWORD, DB_NAME and DB_TABLE

    create user DB_USER with password 'DB_PASSWORD';
    grant select, insert, update on DB_NAME.DB_TABLE to DB_USER;
    

Deploying Fresh-Coffee-Listener app

  1. Installing dependencies: If you are using an ARM-based device like Raspberry-Pi run

    make install-arm

    For other devices having X84 architecture, you can simply run

    make install
  2. Set Variables in makefile

    • COFFEE_AUDIO_PATH: The absolute path of the original coffee machine sound (E.g. /home/pi/coffee-machine-monitoring/sounds/coffee-sound.m4a)
    • SD_DEFAULT_DEVICE: It is an integer value represents the sounddevice input device number. To find your external device number, run python3 -m sounddevice and you will see something like below;
         0 bcm2835 HDMI 1: - (hw:0,0), ALSA (0 in, 8 out)
         1 bcm2835 Headphones: - (hw:1,0), ALSA (0 in, 8 out)
         2 USB PnP Sound Device: Audio (hw:2,0), ALSA (1 in, 0 out)
         3 sysdefault, ALSA (0 in, 128 out)
         4 lavrate, ALSA (0 in, 128 out)
         5 samplerate, ALSA (0 in, 128 out)
         6 speexrate, ALSA (0 in, 128 out)
         7 pulse, ALSA (32 in, 32 out)
         8 upmix, ALSA (0 in, 8 out)
         9 vdownmix, ALSA (0 in, 6 out)
        10 dmix, ALSA (0 in, 2 out)
      * 11 default, ALSA (32 in, 32 out)

    It means that our default device is 2 since the name of the external device is USB PnP Sound Device. So, we will set it as SD_DEFAULT_DEVICE=2 in our case.

    • OFFICE_NAME: it's a string value like Leuven office
    • DB_USER: Your PostgreSQL database username
    • DB_PASSWORD: the password of the specified user
    • DB_HOST: The host of the database
    • DB_PORT: Port number of the database
    • DB_NAME: Name of the database
    • DB_TABLE: Name of the table
  3. Sanity check: Run make run to see if the app works as expected. You can also have a coffee to test whether it captures the coffee machine sound.

  4. Enabling systemd commands to schedule jobs: After configuring coffee_machine_service.service and coffee_machine_service.timer based on your preferences, as shown above, run to fully deploy the app;

    make run-systemctl
  5. Check the coffee_machine.logs file under the project root directory, if the app works as expected

  6. Check service and timer status with the following commands

    systemctl status coffee_machine_service.service

    and

    systemctl status coffee_machine_service.timer

Having Questions / Improvements ?

Feel free to create an issue and we will do our best to help your coffee machine as well :)

Owner
dataroots
Supporting your data driven strategy.
dataroots
A script that publishes power usage data of iDrac enabled servers to an MQTT broker for integration into automation and power monitoring systems

iDracPowerMonitorMQTT This script publishes iDrac power draw data for iDrac 6 enabled servers to an MQTT broker. This can be used to integrate the pow

Lucas Zanchetta 10 Oct 06, 2022
Raspberry Pi Pico and LoRaWAN from CircuitPython

Raspberry Pi Pico and LoRaWAN from CircuitPython Enable LoRaWAN communications on your Raspberry Pi Pico or any RP2040-based board using CircuitPython

Alasdair Allan 15 Oct 08, 2022
Trajectory optimization package for Mini-Pupper robot

Trajectory optimization package for Mini-Pupper robot Purpose of this repository is to provide low-torque and low-impact trajectory for Mini-Pupper qu

Sotaro Katayama 38 Aug 17, 2022
A global contest to grow and monitor your own food with Raspberry Pi

growlab A global contest to grow and monitor your own food with Raspberry Pi A capture from phototimer of my seed tray with a wide-angle camera positi

Alex Ellis 442 Dec 23, 2022
It is a program that displays the current temperature of the GPU and CPU in real time and stores the temperature history.

HWLogger It is a program that displays the current temperature of the GPU and CPU in real time and stores the temperature history. Sample Usage Run HW

Xeros 0 Apr 05, 2022
Automatic CPU speed & power optimizer for Linux

Automatic CPU speed & power optimizer for Linux based on active monitoring of laptop's battery state, CPU usage, CPU temperature and system load. Ultimately allowing you to improve battery life witho

Adnan Hodzic 3.4k Jan 07, 2023
Automatically draw a KiCad schematic for a circuit prototyped on a breadboard.

Schematic-o-matic Schematic-o-matic automatically draws a KiCad schematic for a circuit prototyped on a breadboard. How It Works The first step in the

Nick Bild 22 Oct 11, 2022
[unmaintained] WiFi tools for linux

Note: This project is unmaintained. While I would love to keep up the development on this project, it is difficult for me for several reasons: I don't

Rocky Meza 288 Dec 13, 2022
Mini Pupper - Open-Source,ROS Robot Dog Kit

Mini Pupper - Open-Source,ROS Robot Dog Kit

MangDang 747 Dec 28, 2022
Get the AltAz coordinates for a given object using astropy and output on a OLED screen.

Star Coordinates Get the AltAz coordinates for a given object using astropy and output on a OLED screen. As a very very newcomer to the astronomy scen

Craig Cmehil 1 Jan 31, 2022
A simple program to make MSI Modern 15 speaker and microphone mute led work.

MSI Modern 15 sound led fixup for linux A simple program to fix the MSI Modern 15 speaker and microphone mute LEDs. Installation Requirements pulsectl

Seyed Danial Movahed 4 Oct 18, 2022
HA-Edge-Connector - HA Edge Connector For Python

HA-Edge-Connector 1. Required a. Smartthings Hub & Homeassistant must be in same

chals 21 Dec 29, 2022
Segger Embedded Studio project for building & debugging Flipper Zero firmware.

Segger Embedded Studio project for Flipper Zero firmware Установка Добавить данный репозиторий в качестве сабмодуля в корень локальной копии репозитор

25 Dec 28, 2022
Tool to create 3D printable terrain with integrated path/road part files (Single material 3d printer)

BACKGROUND This has been an ongoing project of mine for a few months now. I run trails a lot and original the goal was to create a function to combine

9 Apr 26, 2022
DOS-like OS for RP2040 basic microcontroller boards

Micropython DOS-like OS for RP2040 microcontroller boards. Check out the demo video at https://www.youtube.com/watch?v=Az_oiq8GE4Y To start the OS typ

RetiredWizard 58 Dec 27, 2022
iot-dashboard: Fully integrated architecture platform with a dashboard for Logistics Monitoring, Internet of Things.

Fully integrated architecture platform with a dashboard for Logistics Monitoring, Internet of Things. Written in Python. Flask applicati

2 Jul 29, 2022
Baseline model for Augmented Home Assistant

Dataset Preparation Step 1. Rename the Virtual-Home output directory to 'vh.[name]', for example: 'vh.door' Make sure the directory contains 100+ fram

Stanford HCI 1 Aug 24, 2022
New armachat based on Raspberry Pi PICO an Circuitpython code

Armachat-circuitpython New Armachat based on Raspberry Pi PICO an Circuitpython code Software working features: send message with header and store to

Peter Misenko 44 Dec 24, 2022
The main aim of this project is to avoid the accidents in shredding ( Waste Recycling Industry )

shredder-Machine-Hand-Safety The main aim of this project is to avoid the accidents in shredding ( Waste Recycling Industry ) . The Basic function of

Shubham Chaudhari 1 Nov 15, 2021
This is a Virtual Keyboard which is simple yet effective to use.

Virtual-Keyboard This is a Virtual KeyBoard which can track finger movements and lets you type anywhere ranging from notepad to even web browsers. It

Jehan Patel 3 Oct 01, 2021