https://github.com/danielathome19/sung-emotionn-detector
A convolutional neural network trained to classify emotions in singing voices.
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 4 DOI reference(s) in README -
○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.1%) to scientific vocabulary
Keywords
classification
cnn
convolutional-neural-networks
deep-learning
emotion-detector
emotion-recognition
machine-learning
music
music-classification
music-cognition
music-information-retrieval
music-perception
ravdess
ravdess-dataset
signal-processing
singing
singing-voice
vocal-analysis
Last synced: 5 months ago
·
JSON representation
Repository
A convolutional neural network trained to classify emotions in singing voices.
Basic Info
- Host: GitHub
- Owner: danielathome19
- License: apache-2.0
- Language: Python
- Default Branch: master
- Homepage: https://doi.org/10.48550/arxiv.2105.00173
- Size: 845 MB
Statistics
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 1
- Releases: 0
Topics
classification
cnn
convolutional-neural-networks
deep-learning
emotion-detector
emotion-recognition
machine-learning
music
music-classification
music-cognition
music-information-retrieval
music-perception
ravdess
ravdess-dataset
signal-processing
singing
singing-voice
vocal-analysis
Created almost 5 years ago
· Last pushed over 1 year ago
https://github.com/danielathome19/Sung-EmotioNN-Detector/blob/master/
# About
EmotioNN is a convolutional neural network (CNN) trained to classify emotions in singing voices.
To find out more, check out the provided research paper:
* "Emotion Recognition of the Singing Voice: Toward a Real-Time Analysis Tool for Singers" (DOI: [10.48550/arxiv.2105.00173](https://doi.org/10.48550/arxiv.2105.00173))
* Also contained in the "PaperAndPresentation" folder is a handout note, the research paper, and presentation of the research.
# Usage
See:
* https://www.youtube.com/watch?v=f9hs8TYyBxU for an overview of analyzing the output data.
* https://www.youtube.com/watch?v=dsruK0GctG4 for a demonstration of the program and features.
**NOTE:** these folders should be placed in the **same** folder as "main.py". For folder existing conflicts, simply merge the directories.
In main.py, the "fmain" function acts as the controller for the model, where calls to train the model, create a prediction, split a wave file, isolate vocals, test in realtime, and all other functions are called. One may also call these functions from an external script ("from main import wavsplit", etc.).
To choose an operation or series of operations for the model to perform, simply edit the main function before running. Examples of all function calls can be seen commented out within main.
# Bugs/Features
Bugs are tracked using the GitHub Issue Tracker.
Please use the issue tracker for the following purpose:
* To raise a bug request; do include specific details and label it appropriately.
* To suggest any improvements in existing features.
* To suggest new features or structures or applications.
# License
The code is licensed under Apache License 2.0.
# Citation
If you use this code for your research, please cite this project:
```bibtex
@software{Szelogowski_Sung-EmotioNN-Detector_2021,
author = {Szelogowski, Daniel},
doi = {10.48550/arxiv.2105.00173},
month = {May},
title = {{Sung-EmotioNN-Detector}},
license = {Apache-2.0},
url = {https://github.com/danielathome19/Sung-EmotioNN-Detector},
version = {1.0.0},
year = {2021}
}
```
Owner
- Name: Daniel J. Szelogowski
- Login: danielathome19
- Kind: user
- Location: Wisconsin
- Company: @MECS-Research-Lab
- Website: https://danielszelogowski.com/
- Twitter: DanielAtHome19
- Repositories: 50
- Profile: https://github.com/danielathome19
Standing on the shoulders of giants.
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