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Bee colony activity/health status using audio analysis

Introduction

The people around Open Source Beehives (OSBH) were directing towards audio from the very beginning and therefore presenting coefficients which where won via machine learning, learned via the audio-samples they had so far.

The output is simply the activity/health status of a bee colony. So far the algorithm can tell whether the colony is dormant, active, pre-, post- or swarming, if the queen is missing or hatching. For more background information about the audio processing, please follow up reading current work status thread in the OSBH Forum. So far, the results are promising.

Details

We forked the "OSBH machine learning" repository to osbh-audioanalyzer to make it able to obtain an input file option. The wrapper script resides in the [audiohealth] repository.

For more information, see also Rate vitality of bee colony via analysing its sound.

Usage

Synopsis

audiohealth --audiofile ~/audio/samples/colony_before_swarming_25_to_15.ogg --analyzer tools/osbh-audioanalyzer/bin/test

Output:

==================
Sequence of states
==================
pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, active, active, active, active, active, active, active, active, active, active, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, pre-swarm, active, active, active, active, active, active, active, pre-swarm, active, pre-swarm, pre-swarm, pre-swarm, pre-swarm, active, active, active, active, pre-swarm, pre-swarm, active, active, pre-swarm, active, pre-swarm, active, active, active, pre-swarm,

===================
Compressed timeline
===================
  0s -  80s   pre-swarm       ========
 90s - 180s   active          =========
190s - 310s   pre-swarm       ============
320s - 380s   active          ======
390s - 400s   pre-swarm       =
400s - 410s   active          =
410s - 440s   pre-swarm       ===
450s - 480s   active          ===
490s - 500s   pre-swarm       =
510s - 520s   active          =
530s - 540s   pre-swarm       =
540s - 550s   active          =
550s - 560s   pre-swarm       =
560s - 580s   active          ==
590s - 600s   pre-swarm       =

==============
Total duration
==============
       320s   pre-swarm       ================================
       280s   active          ============================

======
Result
======
The most common events (i.e. the events with the highest total duration) are:

     The colony is mostly in »PRE-SWARM« state, which is going on for 320 seconds.
     Sometimes, the state oscillates to »ACTIVE«, for 280 seconds in total.

==========
Disclaimer
==========
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. NO LIABILITY FOR ANY DAMAGES WHATSOEVER.

Help

$ audiohealth --help
    Usage:
      audiohealth --audiofile audiofile --analyzer /path/to/osbh-audioanalyzer [--strategy lr-2.0] [--debug] [--keep]
      audiohealth --datfile datfile --analyzer /path/to/osbh-audioanalyzer [--strategy lr-2.0] [--debug]
      audiohealth --version
      audiohealth (-h | --help)

    Options:
      --audiofile=<audiofile>   Process audiofile. Please use sox-compatible input formats.
      --datfile=<datfile>       Process datfile.
      --analyzer=<analyzer>     Path to OSBH audioanalyzer binary
      --strategy=<strategy>     The classification strategy. One of dt-0.90, dt-0.91, dt-1.0, dt-2.0, lr-2.0
      --keep                    Keep (don't delete) downsampled and .dat file
      --debug                   Enable debug messages
      -h --help                 Show this screen

Hint: By using --strategy dt-2.0 or even --strategy dt-1.0, different classification strategies can be toggled to be able to compare results against each other. "dt-" means "decision tree", while "lr-" means "logistic regression".

The cutting edge classification strategy is "lr-2.0", which is also the default setting. See also commit Added new filters and logistic regression classifier. Aaron Makaruk describes it on 2017-07-01 like:

Our classifier has been recently updated to include two new states, and we've moved past decision-tree algorithms to something yielding greater results.

Setup

Repository

git clone --recursive https://github.com/hiveeyes/audiohealth
cd audiohealth

Prerequisites

To relieve your machine from compiling SciPy or NumPy, install the python libraries from your distribution. audiohealth furthermore relies on sox for audio resampling. We also recommend youtube-dl for downloading audio samples from Youtube.

Install some distribution software packages:

apt install python-scipy python-numpy sox libsox-fmt-all youtube-dl

Build the osbh-audioanalyzer:

cd tools/osbh-audioanalyzer/
./build.sh
cd ../..

Main program

virtualenv --system-site-packages .venv27
source .venv27/bin/activate
python setup.py develop

Credits

The driving force behind the audio signal processing at OSBH is Javier Andrés Calvo, so we want to send a big thank you to him and the whole OSBH team - this program is really standing on the shoulders of giants. Keep up the good work!