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.