@inproceedings{f5d0e763e15b4b2bb2e548bf84d11021,
title = "Music mood classification using audio power and audio harmonicity based on MPEG-7 audio features and Support Vector Machine",
abstract = "Music can affect a person's mood. Music psychologists agree that music has a significant impact on a person's mood that determines their behavior. Therefore, our research examines the audio features that affect mood. Our method is to perform feature extraction based on MPEG-7 Low-Level Descriptors. MPEG-7 is international standardized multimedia metadata in ISO/IEC 15938. In this paper, we have made a researched about music mood classification using Audio Power and Audio Harmonicity features. The result of the extraction of the MPEG-7 obtained 17 features low-level descriptors. These features are classified using Support Vector Machine (SVM). There are two stages of SVM: training and prediction phase. Traning phase is when the machine learns to recognize the characteristics of the signal on a label while in prediction phase, it gives the predicted outcome of a label on a new signal characteristic pattern. The success rate of this experiment was 74.28% using Audio Power and Audio Harmonicity, 37.14% using Audio Spectrum Projection, and 28.57% using Audio Power, Audio Harmonicity and Audio Spectrum Projection.",
keywords = "MPEG-7, SVM, music mood classification",
author = "Ridoean, {Johanes Andre} and Riyanarto Sarno and Dwi Sunaryo and Wijaya, {Dedy Rahman}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd International Conference on Science in Information Technology, ICSITech 2017 ; Conference date: 25-10-2017 Through 26-10-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/ICSITech.2017.8257088",
language = "English",
series = "Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "72--77",
editor = "Riza, {Lala Septem} and Andri Pranolo and Wibawa, {Aji Prasetyo} and Enjun Junaeti and Yaya Wihardi and Hashim, {Ummi Raba'ah} and Shi-Jinn Horng and Rafal Drezewski and Lim, {Heui Seok} and Goutam Chakraborty and Leonel Hernandez and Shah Nazir",
booktitle = "Proceeding - 2017 3rd International Conference on Science in Information Technology",
address = "United States",
}