https://github.com/kinwaicheuk/ijcnn2020_music_emotion
Source code for the paper published in IJCNN 2020 "Regression-based Music Emotion Prediction using Triplet Neural Networks"
https://github.com/kinwaicheuk/ijcnn2020_music_emotion
Last synced: 4 days ago
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Source code for the paper published in IJCNN 2020 "Regression-based Music Emotion Prediction using Triplet Neural Networks"
- Host: GitHub
- URL: https://github.com/kinwaicheuk/ijcnn2020_music_emotion
- Owner: KinWaiCheuk
- Created: 2020-04-09T09:46:16.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T22:10:17.000Z (about 2 years ago)
- Last Synced: 2025-02-25T10:23:21.168Z (2 months ago)
- Language: Jupyter Notebook
- Size: 1.72 MB
- Stars: 5
- Watchers: 3
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# IJCNN2020_music_emotion
source code for the paper publised in IJCNN 2020 "Regression-based Music Emotion Prediction using Triplet Neural Networks"
# Dependencies
natsort==7.0.1
tensorflow-GPU==1.13.1
keras==2.3.1
seaborn==0.10.0
scikit-learn==0.19.2
pydub==0.23.1
matplotlib==3.0.2
tqdm
# Instruction
The train the model, please refer to `Training.ipynb`. The model is trained on DEAM dataset, which can be downloaded [here](http://cvml.unige.ch/databases/DEAM/). The data processing steps can be found in `DEAM/Data_Processing.ipynb`.
To use your trained model to predict music emotion, please refer to `Demonstration.ipynb`.
# Demonstration
In each of the region, we picked one song, and the predicted song can be found in `/Demo/*`
[Low](https://raw.githubusercontent.com/KinWaiCheuk/IJCNN2020_music_emotion/master/Demo/Low.mp3)
[UpperLow](https://raw.githubusercontent.com/KinWaiCheuk/IJCNN2020_music_emotion/master/Demo/UpperLow.mp3)
[Middle](https://raw.githubusercontent.com/KinWaiCheuk/IJCNN2020_music_emotion/master/Demo/Middle.mp3)
[LowerHigh](https://raw.githubusercontent.com/KinWaiCheuk/IJCNN2020_music_emotion/master/Demo/LowerHigh.mp3)
[High](https://raw.githubusercontent.com/KinWaiCheuk/IJCNN2020_music_emotion/master/Demo/High.mp3)