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Eng Swee Kheng
Preferred name
Eng Swee Kheng
Official Name
Eng, Swee Kheng
Alternative Name
Swee Kheng, Eng
Kheng, Eng Swee
Main Affiliation
Scopus Author ID
36959579500
Researcher ID
ERV-1458-2022
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PublicationImplementation of music emotion classification using deep learningMusic plays a crucial role in shaping emotions and experiences, making its classification an important area of research with applications in therapy, recommendation systems, and affective computing. This study develops a deep learning-based system to classify music into three emotional categories: "Angry," "Happy," and "Sad." The dataset, consisting of 22 audio files collected from YouTube, was manually labelled, segmented into 30-second clips, and augmented using pitch shifting and time stretching to enhance diversity. Features were extracted using Mel-Frequency Cepstral Coefficients (MFCC) and spectral contrast to analyse the harmonic and timbral characteristics of the audio. Three deep learning models, CNN, CNN-LSTM, and CNN-GRU, were evaluated. CNN-GRU achieved the highest weighted accuracy of 99.10%, demonstrating superior performance. Future work includes adding more emotion categories, diversifying the dataset, exploring advanced architectures like transformers, optimising hyperparameters, implementing real-time applications, and conducting user studies to assess effectiveness. This research successfully developed and evaluated a music emotion classification system, contributing to advancements in the field.
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