Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/psykulsk/rpianc
Active Noise Control on Raspberry Pi
https://github.com/psykulsk/rpianc
activenoisecancellation cpp digital-signal-processing dsp noise-cancellation noise-reduction raspberry raspberry-pi raspberry-pi-3 signal-processing
Last synced: 4 months ago
JSON representation
Active Noise Control on Raspberry Pi
- Host: GitHub
- URL: https://github.com/psykulsk/rpianc
- Owner: psykulsk
- License: mit
- Created: 2018-06-07T09:44:49.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-08-15T19:35:25.000Z (over 1 year ago)
- Last Synced: 2024-10-11T02:02:38.631Z (4 months ago)
- Topics: activenoisecancellation, cpp, digital-signal-processing, dsp, noise-cancellation, noise-reduction, raspberry, raspberry-pi, raspberry-pi-3, signal-processing
- Language: C++
- Homepage:
- Size: 548 KB
- Stars: 115
- Watchers: 7
- Forks: 19
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# RpiANC
Implementation of Active Noise Control Algorithms for Raspberry Pi.
This is the accompanying code to the publication 'Adaptive Active Noise Cancelling System for Headphones on Raspberry Pi Platform'
that has been presented at the Signal Processing Workshop 2020 conference https://mrweek.org/spw/
Link to the article: https://ieeexplore.ieee.org/document/9259141
## Used hardware
* Raspberry Pi 3 Model A+
* Two Tiny MEMS Microphones - https://www.adafruit.com/product/3421
* On-ear headphones connected via 3.5mm jack
## Build and run commands
Example:
```
mkdir build && cd build
cmake ../ && make all
```
Main binary that does feedforward active noise control:
```
./ffANC
```
Simple tests that show how LMS and FxLMS attenuate simulated noise on matplotlib plots:
```
./lmstest
./fxlmstest
```
## Build dependencies
Builds tested on Ubuntu 18.04 and Raspbian distributions:
```
Cmake >= 3.7
Alsa library, so packages like: libasound2, libasound2-dev.
Python2.7 libraries (matplotlibcpp dependency) so packages like: python-dev
OpenMP directives (propably supported by your compiler)
```
## Repository and code structure
Cmake and make commands build a few binaries. The main one, is the `ffANC` binary that executes tha main function from
the [feedforward_anc.cpp](Mains/feedforward_anc.cpp) file. Inside this file you can define or remove the DEPLOYED_ON_RPI
macro to change devices used for capture and playback. Define CAP_MEASUREMENTS macro to capture sample values and save
them to files.
The main function of the `ffANC` target executes a specified number of loop iterations. During each iteration 3 main
operations: capture of new samples, sample processing and playback of the calculated samples, are executed concurrently,
in a fork-join model. Firstly, each of the 3 operations is executed in a separate thread. Then, output samples from each
operation are exchanged. Most recently calculated output samples are moved to the input array of the playback function
and newly captured samples are moved to the input array of the signal processing function.
In [constants.h](Headers/constants.h) file, you can find the names of devices used on Raspberry Pi and other constants used in signal processing.
All signal processing is in the header files under the [Headers](Headers)
and the [processing.cpp](Sources/processing.cpp) source file.
[Scripts](Scripts) directory contains python and bash scripts that automate measurements, gathering data, deploying code etc.
## Results
Comparison of attenuation of a noise signal of a constant frequency between the created ANC system and Sennheiser HR 4.50 BTNC headphones.
Disclaimer: this implementation worked on a specific hardware setup and attenuated a specific (constant frequency) type of noise. It is not guaranteed that it will work out of the box
on a different setup. I've published the code on github so that when someone else is going to try to implement something similar this codebase can be used as a starting point or an inspiration.
## Useful links, articles, etc.
* [Microphone wiring and connection guide](https://learn.adafruit.com/adafruit-i2s-mems-microphone-breakout/raspberry-pi-wiring-test#wiring-for-stereo-mic-3061608-5)
* [Introduction to Sound Programming with ALSA](https://www.linuxjournal.com/article/6735)
* [A Tutorial on Using the ALSA Audio API](http://equalarea.com/paul/alsa-audio.html)
* [Rpi I2S thread](https://www.raspberrypi.org/forums/viewtopic.php?t=91237)
* [Implementation of FIR Filtering in C](https://sestevenson.wordpress.com/implementation-of-fir-filtering-in-c-part-1/)
## Included third-party libraries
This software uses matplotlibcpp - see matplotlibcpp-license.txt