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Emotion Prediction in Music Based on Artificial Intelligence Techniques

Music is often described as the "language of emotion,"and emotion prediction is important as it can impact future behavior. This paper proposes an audio-based emotion prediction model using a One-Dimensional Convolutional Neural Network (1D-CNN) approach, with Mel-Frequency Cepstral Coeffi...

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Main Authors: Lin, Y.-J., Ding, S.Y., Lu, C.-K., Tang, T.B., Shen, J.-Y.
Format: Conference or Workshop Item
Published: 2023
Online Access:http://scholars.utp.edu.my/id/eprint/38034/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174920802&doi=10.1109%2fICCE-Taiwan58799.2023.10226902&partnerID=40&md5=b23cc77ba7b805f2f7344ab3587814a1
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id oai:scholars.utp.edu.my:38034
record_format eprints
spelling oai:scholars.utp.edu.my:380342023-12-11T03:09:38Z http://scholars.utp.edu.my/id/eprint/38034/ Emotion Prediction in Music Based on Artificial Intelligence Techniques Lin, Y.-J. Ding, S.Y. Lu, C.-K. Tang, T.B. Shen, J.-Y. Music is often described as the "language of emotion,"and emotion prediction is important as it can impact future behavior. This paper proposes an audio-based emotion prediction model using a One-Dimensional Convolutional Neural Network (1D-CNN) approach, with Mel-Frequency Cepstral Coefficients (MFCCs) extracted as audio features. Preliminary results show an overall accuracy of 93, but the imbalanced dataset used may cause bias in the accuracy of each emotion. Further research is needed to investigate the classification of audio features and 1D-CNN layers. © 2023 IEEE. 2023 Conference or Workshop Item NonPeerReviewed Lin, Y.-J. and Ding, S.Y. and Lu, C.-K. and Tang, T.B. and Shen, J.-Y. (2023) Emotion Prediction in Music Based on Artificial Intelligence Techniques. In: UNSPECIFIED. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174920802&doi=10.1109%2fICCE-Taiwan58799.2023.10226902&partnerID=40&md5=b23cc77ba7b805f2f7344ab3587814a1 10.1109/ICCE-Taiwan58799.2023.10226902 10.1109/ICCE-Taiwan58799.2023.10226902 10.1109/ICCE-Taiwan58799.2023.10226902
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Music is often described as the "language of emotion,"and emotion prediction is important as it can impact future behavior. This paper proposes an audio-based emotion prediction model using a One-Dimensional Convolutional Neural Network (1D-CNN) approach, with Mel-Frequency Cepstral Coefficients (MFCCs) extracted as audio features. Preliminary results show an overall accuracy of 93, but the imbalanced dataset used may cause bias in the accuracy of each emotion. Further research is needed to investigate the classification of audio features and 1D-CNN layers. © 2023 IEEE.
format Conference or Workshop Item
author Lin, Y.-J.
Ding, S.Y.
Lu, C.-K.
Tang, T.B.
Shen, J.-Y.
spellingShingle Lin, Y.-J.
Ding, S.Y.
Lu, C.-K.
Tang, T.B.
Shen, J.-Y.
Emotion Prediction in Music Based on Artificial Intelligence Techniques
author_facet Lin, Y.-J.
Ding, S.Y.
Lu, C.-K.
Tang, T.B.
Shen, J.-Y.
author_sort Lin, Y.-J.
title Emotion Prediction in Music Based on Artificial Intelligence Techniques
title_short Emotion Prediction in Music Based on Artificial Intelligence Techniques
title_full Emotion Prediction in Music Based on Artificial Intelligence Techniques
title_fullStr Emotion Prediction in Music Based on Artificial Intelligence Techniques
title_full_unstemmed Emotion Prediction in Music Based on Artificial Intelligence Techniques
title_sort emotion prediction in music based on artificial intelligence techniques
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/38034/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174920802&doi=10.1109%2fICCE-Taiwan58799.2023.10226902&partnerID=40&md5=b23cc77ba7b805f2f7344ab3587814a1
_version_ 1787138258075910144
score 13.214268

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