SentiMozart: Music Generation based on Emotions

Rishi Madhok, Shivali Goel, Shweta Garg

2018

Abstract

Facial expressions are one of the best and the most intuitive way to determine a person’s emotions. They most naturally express how a person is feeling currently. The aim of the proposed framework is to generate music corresponding to the emotion of the person predicted by our model. The proposed framework is divided into two models, the Image Classification Model and the Music Generation Model. The music would be generated by the latter model which is essentially a Doubly Stacked LSTM architecture. This is to be done after classification and identification of the facial expression into one of the seven major sentiment categories: Angry, Disgust, Fear, Happy, Sad, Surprise and Neutral, which would be done by using Convolutional Neural Networks (CNN). Finally, we evaluate the performance of our proposed framework using the emotional Mean Opinion Score (MOS) which is a popular evaluation metric for audio-visual data.

Paper Citation

in Harvard Style

Madhok R., Goel S. and Garg S. (2018). SentiMozart: Music Generation based on Emotions.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 501-506. DOI: 10.5220/0006597705010506

in Bibtex Style

@conference{icaart18,
author={Rishi Madhok and Shivali Goel and Shweta Garg},
title={SentiMozart: Music Generation based on Emotions},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={501-506},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006597705010506},
isbn={978-989-758-275-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - SentiMozart: Music Generation based on Emotions
SN - 978-989-758-275-2
AU - Madhok R.
AU - Goel S.
AU - Garg S.
PY - 2018
SP - 501
EP - 506
DO - 10.5220/0006597705010506