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https://github.com/lmangani/voip-classifier
Simple kNN classifier for VoIP and RTC Metrics
https://github.com/lmangani/voip-classifier
jitter k-nearest-neighbours knn machine-learning mean-opinion-score ml mos packet-loss rtc rtt voip
Last synced: about 1 month ago
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Simple kNN classifier for VoIP and RTC Metrics
- Host: GitHub
- URL: https://github.com/lmangani/voip-classifier
- Owner: lmangani
- Created: 2017-11-08T00:14:13.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2017-11-08T12:01:28.000Z (about 7 years ago)
- Last Synced: 2024-10-04T13:21:46.385Z (4 months ago)
- Topics: jitter, k-nearest-neighbours, knn, machine-learning, mean-opinion-score, ml, mos, packet-loss, rtc, rtt, voip
- Language: JavaScript
- Homepage: http://qxip.net
- Size: 9.77 KB
- Stars: 3
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# VoIP-classifier
Simple kNN classifier for VoIP and RTC Metrics
**kNN** stands for *k-Nearest-Neighbours*, which is a Supervised machine learning algorithm used for classification, determining the class of a data point based on the maximum number of neighbors the data point has belonging to the same class.
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## MOS Classifier
**MOS** stands for Mean Opinion Score, a commonly used measure for audio and video VoIP quality evaluation. This example only accounts for network performance related parameters negatively affecting the score.
#### Dataset Warning
In this example, a fictional Data set is provided for trainig ml.js KNN module using various combinations of Packet Loss, Jitter and Round-Trip-Tip measurements and their resulting MOS rank in class 1-4. This dataset is oversimplified, purely illustrative for educational purposes and does _not_ necessarily represent actual conditions.
## Examples
#### Optimal Values
```
prompt: Lost%/10: 0.0 (0%0
prompt: Jitter/100: 0.5 (50ms)
prompt: RTT/100: 1.0 (100ms)
prompt: CodecType: 0 (PCMU)
With 0,0.5,1,0 -- type = MOS4
```
#### High Packet Loss (50%)
```
prompt: Lost%/10: 0.5 (50%)
prompt: Jitter/100: 1.0 (100ms)
prompt: RTT/100: 1.0 (100ms)
prompt: CodecType: 0 (PCMU)
With 0.5,1,1,0 -- type = MOS1
```
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### Credits
This mere adaption is heavily based on the awesome [Machine Learning with JavaScript](https://hackernoon.com/machine-learning-with-javascript-part-2-da994c17d483) tutorial by [Abhishek Soni](https://github.com/abhisheksoni27)