Adapted PorCC to python
Project description
PyPorCC
PyPorCC is a package that allows the detection and classification of Harbor Porpoises' clicks. The detection of clicks in continuous files is a python adaptation of the PAMGuard click detector algorithm.
Gillespie D, Gordon J, McHugh R, McLaren D, Mellinger DK, Redmond P, Thode A, Trinder P, Deng XY (2008) PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localisation of cetaceans. Proceedings of the Institute of Acoustics 30:54–62.
The classification is done using the PorCC algorithm, adapted to python from the paper:
Cosentino, M., Guarato, F., Tougaard, J., Nairn, D., Jackson, J. C., & Windmill, J. F. C. (2019). Porpoise click classifier (PorCC): A high-accuracy classifier to study harbour porpoises ( Phocoena phocoena ) in the wild. The Journal of the Acoustical Society of America, 145(6), 3427–3434. https://doi.org/10.1121/1.5110908
Also other models can be trained. The implemented ones so far are:
svc
: Support Vector Machineslsvc
: Linear Support Vector MachinesRandomForest
: Random Forestknn
: K-Nearest Neighbor
Usage
Click detector
The Click detector can be used in continuous wav files (with higher than 300 kHz sampling rate) or in the SoundTrap HF output files (*.bcl + *.dwv). For SoundTrapHF files, you can create a ClickDetectorSoundTrapHF object with the necessary parameters and run it as:
import pathlib
import pyhydrophone as pyhy
from pyporcc import ClickDetectorSoundTrapHF, ClickDetector, PorCC, Filter
sound_folder = pathlib.Path("./../tests/test_data/soundtrap")
save_folder = pathlib.Path('./../tests/test_data/output')
# Hydrophone
model = 'ST300HF'
name = 'SoundTrap'
serial_number = 67416073
soundtrap = pyhy.soundtrap.SoundTrapHF(name=name, model=model, serial_number=serial_number)
# Filters parameters
lowcutfreq = 100e3 # Lowcut frequency
highcutfreq = 160e3 # Highcut frequency
# Define the filters
pfilter = Filter(filter_name='butter', filter_type='bandpass', order=4,
frequencies=[lowcutfreq, highcutfreq])
dfilter = Filter(filter_name='butter', filter_type='high', order=4, frequencies=20000)
classifier = PorCC(load_type='manual', config_file='default')
cd = ClickDetectorSoundTrapHF(hydrophone=soundtrap, save_folder=save_folder, convert=True,
classifier=classifier, prefilter=pfilter, save_noise=False)
cd.detect_click_clips_folder(sound_folder, blocksize=60 * 576000)
For continuous data, just make sure you use the class ClickDetector object instead of a ClickDetectorSoundTrapHF! The rest of the code should be the same (except the hydrophone definition, which will depend on the instrument you use)
Note
Please note, the clicks PAMGuard's Click Classifier classified as porpoise clicks appear as 0 in both ClassifiedAs and ManualAssign fields.
Citation
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file pyporcc-0.1.3.tar.gz
.
File metadata
- Download URL: pyporcc-0.1.3.tar.gz
- Upload date:
- Size: 24.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b7484314b476e925627b951e9bc1cb9d2b75d4d65175b0da9585289e7cc15d2e |
|
MD5 | 6d31708d6a2b9e473ed9484fa3654595 |
|
BLAKE2b-256 | fbcb15dc0e7b110248765477faa4655aefb37086dd835cecc4e05fda20237d25 |