Vibrotactile quality metrics and metric fusion
Project description
VibroMAF - Vibrotactile Multi-Method Assessment Fusion
Vibrotactile Quality Metrics
This package provides implementations of existing vibrotactile quality metrics and machine learning approach that fuses the individual metric scores into a single final quality score.
Installation
Install vibromaf
from PyPI:
pip install vibromaf
For development versions and alternative installations see the installation documentation.
Usage
Citation
If you use this work, please cite our paper (PDF)
@inproceedings{noll_vibromaf,
author = {Noll, Andreas and Hofbauer, Markus and Muschter, Evelyn and Li, Shu-Chen and Steinbach, Eckehard},
booktitle = {2022 IEEE Haptics Symposium (HAPTICS)},
title = {Automated Quality Assessment for Compressed Vibrotactile Signals Using Multi-Method Assessment Fusion},
year = {2022},
volume = {},
number = {},
pages = {1-6},
doi = {10.1109/HAPTICS52432.2022.9765599},
address = {Santa Barabara, California, USA}
}
Contribute
See our CONTRIBUTING.md
Changelog
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
vibromaf-0.0.8.tar.gz
(18.2 kB
view details)
Built Distribution
vibromaf-0.0.8-py3-none-any.whl
(11.2 kB
view details)
File details
Details for the file vibromaf-0.0.8.tar.gz
.
File metadata
- Download URL: vibromaf-0.0.8.tar.gz
- Upload date:
- Size: 18.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | a6d70c05dc96514f629257be2d974381c3267cb10052c5b66b32595e23daed7a |
|
MD5 | 7797b9ac41465c951635fdb0e05da892 |
|
BLAKE2b-256 | f9e771843c35436f383f6e63590f12c6b7509dd92e57fb54a143cfa475fee71a |
File details
Details for the file vibromaf-0.0.8-py3-none-any.whl
.
File metadata
- Download URL: vibromaf-0.0.8-py3-none-any.whl
- Upload date:
- Size: 11.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ba75d5941b4e1b1a65cec795fde65ffd557038b0321010b60a6987b603ac70ed |
|
MD5 | 90cb50de20fcabd233a22d20ba4d1615 |
|
BLAKE2b-256 | 13a1508f4f5d407260142719e6b5627a400ea1bddc04b3abf1aa9184c766affb |