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- W4386323453 abstract "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." @default.
- W4386323453 created "2023-09-01" @default.
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- W4386323453 date "2023-07-17" @default.
- W4386323453 modified "2023-09-27" @default.
- W4386323453 title "Emotion Prediction in Music Based on Artificial Intelligence Techniques" @default.
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- W4386323453 doi "https://doi.org/10.1109/icce-taiwan58799.2023.10226902" @default.
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