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
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
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
|