Hangdrum

Firmware for a MIDI hang-drum using capacitive touch pads.

Blogposts

• Host-based simulation for embedded systems
• Optimizing latency of an Arduino MIDI controller

Status

In production

• Used as the firmware for dhang since March 2017
• Tested on Arduino Lenonardo (Atmega 32u4)
• Latency until triggered sound heard below 20ms with 8 pads, using Windows with ASIO4LL 96samples
• Detection latency around 1ms per pads

Installing firmware

1. Download repository from Github, or use git to clone.
2. Install the CapacitiveSensor Arduino library
3. Open hangdrum.ino in Arduino IDE
4. Make sure that the hardware pin configuration is correct
5. Flash the Arduino sketch to device

Hardware setup

See CapSense documentation

Architecture

The core of the firmware is platform and I/O independent, written in C++11. It can run on a Arduino-compatible microcontroller (tested on Arduino Leonardo), or on a host computer (tested on Arch Linux).

A single State datastructure holds all state. The program logic is expressed as a pure function of new Input and current State: State next = calculateState(const Input inputs, const State current). Both Input and State are plain-old-data which can be safely serialized and de-serialized. The Hardware Abstraction Layer, which has real-life side-effects, consists of: A function to read current Input, and a function to "realize" a State.

This formulation allows us to:

• trace the execution of the program, by capturing and storing the Input, State pairs.
• replay a trace, by taking the Input and applying it to a modified program
• visualize a whole-program execution from its trace, both end-results and intermediates
• test a whole-program execution, by applying checks against the generated trace
• simulate new scenarios by synthesizing or mutating Input

Wanderers of non-traditional programming methods may recognize inspirations from (Extended) Finite State Machines, Functional Reactive Programming and Dataflow/Flow-based-programming. And basically a rejection of Object Oriented Programming (as typically practiced in C++/Java/Python/..), particularly the idea of combining data and methods that operate on the data into a single class.

Tools

There is an decent set of analysis, simulation and testing tools available.

• tools/logserial.py: Record capacitive sensor input from device.
• tools/plot.py: Plot a stream of sensor data
• bin/simulator: Run the firmware on host. Takes a sensor stream as input. Can run in real-time, producing ALSA MIDI output. Or in faster-than-realtime, producing a Flowtrace of the entire run.
• tools/plotflowtrace.py: Plot a flowtrace produced by simulator, showing decisions made
• tools/sendserial.py: Send an input sensor stream to device